JP3146517B2 - Image processing device - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus having an image restoration circuit for restoring pseudo halftone binary image data into multivalued image data corresponding to an original image.

[0002]

2. Description of the Related Art Conventionally, in a facsimile apparatus, an image signal is transmitted via a public telephone line. Is converted into pseudo-halftone binary image data and transmitted to the receiving side, while the receiving side restores the received pseudo-halftone binary image data to multi-level image data. A method is used. In recent years, a color laser printer that records multi-valued image data at high speed and high resolution has been put into practical use. In general, a binary printer that records binary image data is often used. When storing multi-valued image data in a storage device, a large-capacity storage device is required. In addition, there has been proposed an apparatus for reading out the binary image data from the storage device and restoring the binary image data into multi-valued image data during image processing or recording.

A multi-value restoration image processing apparatus (hereinafter, referred to as a first conventional apparatus) for converting this type of pseudo halftone binary image data into multi-value image data is disclosed in, for example, Japanese Patent Application Laid-Open No. HEI 9 (1994) -259. It is disclosed in JP-A-2-165775. This first
The prior art apparatus includes an image identification unit that detects the presence / absence of the periodicity of the pixel information from the distribution of the pixel information that constitutes the input image represented by the pseudo-gradation, and a change in the density of the pixel information. Density change detecting means to be detected, and smoothing processing is performed on an opening size corresponding to the detected cycle for a portion where the gradation change is gradual detected by the density change detecting means, and for a portion where the gradation change is severe, A multi-valued image corresponding to the input image is estimated by performing a smoothing process with an aperture size smaller than that, and performing a smoothing process with a minimum aperture size on a portion where the periodicity is not detected by the image identification unit. And output smoothing processing means. That is, in the device of the first conventional example, the pseudo-gradation is performed by switching the opening size of the window used for the smoothing process according to the change in the density of the detected pixel information and the presence or absence of the periodicity of the detected pixel information. The image is more faithfully restored to a multi-valued image.

Further, it is determined whether the image data is binary image data binarized in pseudo halftone or binary image data in non-halftone, and according to the image area discrimination result. An image processing apparatus (hereinafter referred to as a second conventional apparatus) for selectively switching between two types of image restoration processing to restore multi-valued image data is disclosed in, for example, Japanese Patent Application No. Hei 3-100961. Proposed. The second prior art apparatus includes an input device that includes binary image data binarized in pseudo halftone and binary image data binarized in non-halftone using a predetermined threshold value. Based on the obtained binary image data, for each pixel of the input binary image data, a region of pseudo halftone binary image data or a region of non-halftone binary image data is used. Discriminating means for discriminating whether or not the input binary image data is restored to multi-gradation image data when the discriminating means determines that the area is the area of pseudo-halftone binary image data; On the other hand, there is provided a restoring means for outputting the inputted binary image data without restoring it when the discriminating means discriminates the area as the non-halftone binary image data area. . In the device of the second conventional example, binary image data binarized in pseudo halftone and binary image data binarized in non-halftone using a predetermined threshold value are input. When restored binary image data is restored to multi-gradation image data, no blur occurs in the non-halftone area, a higher quality image can be obtained, and furthermore, binarization is performed by a pseudo halftone method. There is an advantage that the binary image data obtained can be restored to multi-gradation image data.

[0005]

In various types of image processing, there are actually various image readers and various pseudo halftone binarization circuits. However, in the above-described apparatus of the related art, depending on the change in the density of the detected pixel information and the presence or absence of the periodicity of the detected pixel information (in the case of the first related art apparatus) or according to the image area determination result. (In the case of the second conventional example), since the restoration processing for restoring to multi-valued image data is selectively switched, image data read by various image readers or various pseudo halftone binary values are obtained. Image data binarized by pseudo-halftones by the conversion circuit, it is often the case that an error occurs in the detection result or the image area determination result. Therefore, the image data binarized in the non-halftone using the predetermined threshold value and the image data binarized in the pseudo halftone are faithfully restored to the multivalued image data corresponding to the original image. There was a problem that it was not possible. SUMMARY OF THE INVENTION An object of the present invention is to solve the above problems, and to more faithfully restore multi-valued image data corresponding to an original image based on various input binary image data as compared with the conventional example. It is an object of the present invention to provide an image processing apparatus capable of performing the above.

[0006]

An image processing apparatus according to a first aspect of the present invention uses binary image data binarized in pseudo halftone and non-halftone binary image data using a predetermined threshold value. In the input binary image data including the binarized binary image data, two pixels corresponding to the pixel of interest and a plurality of pixels around the pixel of interest.
Based on the value image data, for each pixel of the input binary image data, a first type pseudo halftone image-likeness and a second type pseudo halftone different from the first type pseudo halftone Determining means for determining the likelihood of a halftone image and calculating a determination value indicating the likelihood of a pseudo-halftone image and the likelihood of a non-halftone image using the results of these determinations; Is restored to multi-valued image data by a restoration process in a case where the binary image data is pseudo halftone image data for each pixel based on the binary image data corresponding to the pixel of interest and a plurality of pixels surrounding the pixel of interest. And first restoration means for restoring the binary image data to multi-valued image data by a restoration process when the binary image data is the non-halftone image data for each pixel based on the input binary image data. Second restoration means Mixing the multi-valued image data restored by the first restoration means and the multi-valued image data restored by the second restoration means at a mixing ratio corresponding to the discrimination value calculated by the discrimination means. And mixing means for outputting the mixed multi-valued image data. In the image processing apparatus, the determination unit preferably determines a first-type pseudo-halftone image-likeness result and a second-type pseudo-halftone image for a plurality of pixels including a target pixel. Using the discrimination result of likelihood, a discrimination value indicating the likelihood of a pseudo halftone image and the likelihood of a non-halftone image for the pixel of interest is calculated. Here, the first type pseudo-halftone image likeness is preferably a dot concentration type pseudo halftone image-likeness.

[0007] An image processing apparatus according to a second invention of the present application comprises:
Of the input binary image data including the binary image data binarized by the pseudo halftone and the binary image data binarized by the non-halftone using a predetermined threshold value, Based on the binary image data corresponding to the target pixel and a plurality of pixels around the target pixel, for each pixel of the input binary image data,
Discriminating means for calculating a discriminant value indicating the likelihood of a pseudo halftone image and the likeness of a non-halftone image; and a binary corresponding to the pixel of interest and a plurality of pixels around the pixel of interest in the input binary image data First restoration means for restoring to multi-valued image data by restoration processing when the binary image data is pseudo halftone image data for each pixel based on the image data, and the inputted binary image data Is restored to multi-valued image data by a restoration process when the binary image data is the non-halftone image data for each pixel based on
Mixing the multi-valued image data restored by the first restoration unit and the multi-valued image data restored by the second restoration unit according to the discrimination value calculated by the discrimination unit. Mixing means for mixing the mixed multi-valued image data and outputting the mixed multi-valued image data, wherein the discriminating means determines, for each pixel of the input binary image data, the likelihood of a dot-dispersed pseudo-halftone image. A first discriminating means for calculating a discriminating value indicating the likeness, and a second discriminating means for calculating a discriminating value indicating the likelihood of a dot-concentrated pseudo halftone image for each pixel of the input binary image data. Comparing the discrimination value calculated by the first discrimination means with the discrimination value calculated by the second discrimination means to select a larger discrimination value, and setting the selected discrimination value to the pseudo halftone Image quality and non-medium Characterized by comprising a comparison and selection means for outputting a discrimination value indicating an image likeness of the tone.

[0008]

In the image processing apparatus according to the first aspect of the present invention, the discriminating means uses the binary image data binarized by the pseudo halftone and the non-halftone binary image data by using a predetermined threshold value. The input binary image data based on the binary image data corresponding to the target pixel and a plurality of pixels around the target pixel among the input binary image data including the binarized binary image data. For each pixel, the likelihood of a first type of pseudo-halftone image and the likeness of a second type of pseudo-halftone image different from the first type of pseudo-halftone are determined, and the results of these determinations are used. Then, a discrimination value indicating the likelihood of a pseudo halftone image and the likeness of a non-halftone image is calculated. Next, the first restoring means performs a binary image data conversion for each pixel based on the binary image data corresponding to the pixel of interest and a plurality of pixels around the pixel of interest in the input binary image data. Is restored to multi-valued image data by a restoration process when the image data is pseudo halftone image data. On the other hand, the second restoration means performs a binary image processing for each pixel based on the input binary image data. When the data is the non-halftone image data, the data is restored to multi-valued image data by the restoration process. Further, the mixing means may convert the multi-valued image data restored by the first restoration means and the multi-valued image data restored by the second restoration means in accordance with the discrimination value calculated by the discrimination means. And outputs the mixed multi-valued image data. That is, without selectively switching and performing the restoration process of restoring to multi-valued image data as in the conventional example, the discrimination value indicating the likelihood of a predetermined pseudo halftone image and the non-halftone image is calculated. The multi-valued image data restored by the restoration process when the binary image data is the predetermined pseudo halftone image data and the binary image data are mixed at the mixing ratio according to the calculated discrimination value. Since the target multi-valued image data is obtained by mixing the multi-valued image data restored by the restoration process in the case of non-halftone image data, various image readers and various pseudo half tone binary values are obtained. A restoration process to multi-valued image data can be performed according to the determination value corresponding to the conversion circuit. Therefore, image data binarized in a non-halftone using a predetermined threshold value and image data binarized in various pseudo-halftones correspond to the original image more faithfully as compared with the conventional example. Can be restored to multi-valued image data.

In the above-mentioned image processing apparatus, preferably, the discriminating means includes a discriminating result of a first kind of pseudo-halftone image-likeness for a plurality of pixels including a pixel of interest and a second kind of pseudo-halftone. The discrimination value indicating the pseudo-halftone image-likeness and the non-halftone image-likeness for the pixel of interest is calculated using the tonal image-likeness discrimination result. Here, the first type pseudo-halftone image likeness is preferably a dot concentration type pseudo halftone image-likeness.
That is, the pseudo-halftone for the target pixel is determined using the determination result of the first-type pseudo-halftone image-likeness and the second-type pseudo-halftone image-ness determination result for a plurality of pixels including the target pixel. Since the discrimination value indicating the image likeness of the image and the non-halftone image likeness is calculated, the accuracy of the discrimination value can be further improved.

Further, in the image processing apparatus according to the second aspect of the present invention, the discriminating means uses the pseudo-halftone binary image data and the non-halftone using a predetermined threshold value. Based on the binary image data corresponding to the pixel of interest and a plurality of pixels surrounding the pixel of interest, of the input binary image data including the binary image data binarized by For each pixel of the image data, a discrimination value indicating the likelihood of a pseudo halftone image and the likeness of a non-halftone image is calculated. Next, the first restoring means outputs the input 2
For each pixel, based on binary image data corresponding to the pixel of interest and a plurality of pixels surrounding the pixel of interest in the value image data,
While the binary image data is restored to the multi-valued image data by the restoration process when the image data is the pseudo halftone image data, the second restoration unit performs the restoration for each pixel based on the input binary image data. And restores to multi-valued image data by a restoration process when the binary image data is the non-halftone image data. Further, the mixing means may convert the multi-valued image data restored by the first restoration means and the multi-valued image data restored by the second restoration means in accordance with the discrimination value calculated by the discrimination means. And outputs the mixed multi-valued image data. Here, in the determining means, the first determining means calculates, for each pixel of the input binary image data, a determining value indicating the likelihood of a dot dispersed pseudo halftone image, Determining means for each pixel of the input binary image data, calculates a determination value indicating the likelihood of a dot-concentrated pseudo halftone image, and comparing and selecting means calculates by the first determining means The selected discrimination value is compared with the discrimination value calculated by the second discriminating means to select a larger discrimination value, and the selected discrimination value is expressed by the pseudo-halftone image and the non-halftone image. Is output as a discrimination value indicative of. In general, a non-halftone image is neither a dot-dispersed pseudo-halftone image nor a dot-concentrated pseudo-halftone image. Therefore, by selecting a discrimination value as described above, the predetermined pseudo-halftone image can be obtained. It is possible to obtain a discrimination value in consideration of both the halftone image and the non-halftone image. By mixing the restored multi-valued image data as described above using the obtained discrimination value,
Multi-valued image data more faithful to the original image can be restored.

[0011]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A facsimile apparatus according to an embodiment of the present invention will be described below with reference to the drawings. Here, the facsimile apparatus of the present embodiment is characterized in that it has an image restoration processing unit 62 for restoring received binary image data to multi-valued image data as shown in FIG. In addition,
In the following description of the embodiments, “halftone image” and “halftone region” are respectively obtained by binarizing multivalued image data of a halftone image such as a photograph by a pseudo halftone method such as a dither method. The term “pseudo-halftone image” and the area of the image mean the “non-halftone image” and the “non-halftone area” refer to the non-halftone image such as a character and the area of the image, respectively.

A facsimile apparatus according to an embodiment of the present invention will be described in the following order. (1) Features of the present embodiment (2) Configuration and operation of facsimile apparatus (3) Image restoration processing unit (4) 10 × 21 matrix memory circuit (5) Image area determination unit (5-1) Configuration and operation of each unit (5-2) dither determination unit (5-3) adjacent state determination unit, (5-4) 5 × 11 matrix memory circuit (5-5) determination data generation unit (5-6) determination data signal generation unit (6) ) Halftone image restoration unit (6-1) Configuration and operation of each unit (6-2) In-window counting unit (6-3) Smoothing amount calculation unit (6-4) Edge enhancement amount calculation unit (6-5) Edge Area discriminator (6-6) Restored data calculator (7) Another embodiment

(1) Features of the present embodiment As shown in FIG. 3, the facsimile apparatus of the first embodiment uses binary image data binarized by pseudo halftone and a predetermined threshold value. A predetermined edge enhancement amount, a predetermined smoothed value, and a predetermined edge region discrimination amount are calculated based on received binary image data including binary image data binarized using non-halftones, and will be described later. The centralized-type first dither image determining signal and the centralized-type second dither image determining signal, which are input from the determining data signal generating unit 114 and indicate the determination results of the respective concentrated centralized dither images, and the respective calculated amounts A halftone image restoration unit 101 for restoring binary image data input based on the binary image data based on the received binary image data, and a predetermined area centered on a pixel of interest based on the received binary image data for each pixel. In the prescribed A centralized first dither image determination signal and a centralized second dither image determination signal indicating a determination result of a concentrated dither image are generated, and it is determined whether the image is a halftone area or a non-halftone area. An image area discriminating unit 102 that outputs image area discrimination data indicating a result; and a multi-valued non-halftone image that indicates white or black by converting non-halftoned binary image data using a predetermined threshold value A simple multi-value conversion unit 103 that simply converts the image data into image data; a multi-value half-tone image data output from the half-tone image restoration unit 101 according to the mixture ratio indicated by the image area determination data; And a data mixing unit 104 that generates multi-valued image data by mixing the multi-valued non-halftone image data output from the unit 103 and outputs the multi-valued image data to the printer control unit 55.

Here, in particular, the halftone image restoring unit 101 and the image area discriminating unit 102 are characterized by
Reference numeral 01 denotes (a) an in-window counting unit 1 that counts the number of black pixels in a plurality of predetermined windows based on input pixel data and outputs black pixel number data (hereinafter abbreviated as data).
13 and (b) an edge area discriminating unit 109 that calculates and outputs an edge area discriminating amount used for discriminating an edge area based on data output from the in-window counting unit 113.
And (c) a smoothing amount (7 × 7) for restoring halftone image data based on the data output from the intra-window counting unit 113.
A smoothing amount calculating unit 110 which calculates and outputs the second data of the number of black pixels and the 9 × 9 black pixel number data), and (d) an in-window counting unit 1
Edge enhancement amount first data and edge enhancement amount second data for performing an edge enhancement process based on the data output from
Edge enhancement amount calculator 111 that calculates and outputs data
And (e) multi-level halftone based on the data output from each of the units 109 to 111 and the concentrated first dither image determination signal and the concentrated second dither image determination signal output from the determination data signal generation unit 114. A restoration data calculation unit 112 for restoring and outputting image data.

The image area discriminating section 102 (a) determines, based on the input pixel data, the adjacent state of the smaller number of the same kind of pixels in a predetermined area in four main and sub scanning directions. Calculate the number of neighbors in the main and sub scanning directions indicating
Black pixel number data in a predetermined 3 × 3 window is calculated, and based on the calculated data and the black pixel number data in a predetermined 7 × 7 window output from the dither determination unit 106, An all-white / all-black image detection signal indicating whether the inside of the 7 × 7 window is an all-white image or an all-black image, and that an image in a predetermined area centered on the pixel of interest is a halftone image. An adjacent state determination unit 105 for generating and outputting a halftone image detection signal indicating that the image in the predetermined area is a non-halftone image and a non-halftone image detection signal indicating that the image in the predetermined area is a non-halftone image; Based on the pixel data, for each pixel, it detects whether or not the image is a concentrated systematic dither image having a screen angle of 0 degrees, and outputs a concentrated first dither detection signal indicating a detection result. Centralized set with a 45 degree angle A dither determination unit 106 for outputting a centralized second dither detection signal indicating the detection result by detecting whether the dither image, (c) adjacent state determination section 10
5 and five detection signals (total 5 bits) serially output for each pixel from the dither determination unit 106 are simultaneously output within a predetermined 5 × 11 window centered on the pixel of interest. (D) a matrix memory circuit 107;
A determination data generation unit that generates and outputs determination data obtained by adding the detection signals within the 5 × 11 window for each detection signal based on each detection signal output from the matrix memory circuit 107 108, and (e) the image of the predetermined 5 × 11 window area is a centralized systematic dither image having a screen angle of 0 degrees based on each determination data output from the determination data generation unit 108. A centralized first dither image determination signal indicating the determination result is generated and output, and the image of the predetermined 5 × 11 window area is a centralized systematic image having a screen angle of 45 degrees. A centralized-type second dither image discrimination signal indicating the discrimination result is generated and output, and the result of the discrimination as to whether the image is a halftone area or a non-halftone area is determined. The and a discrimination data signal generating unit 114 for outputting the image area determination data shown.

(2) Configuration and Operation of Facsimile Apparatus FIG. 1 is a vertical sectional view of a mechanism of a facsimile apparatus according to a first embodiment of the present invention, and FIG. 2 is a facsimile apparatus shown in FIG. FIG. 3 is a block diagram illustrating a configuration of a signal processing unit. As shown in FIG. 1, the facsimile apparatus includes a printer unit 1 and an image reading unit 2 installed above the printer unit.
And an operation panel 4 on the printer unit 1.
0 is provided, and a telephone 4
2 are provided.

In FIG. 1, a printer unit 1 is an electrophotographic laser beam printer having a configuration similar to that of a conventional apparatus, and its operation will be briefly described below. First, the photoconductor on the photoconductor drum 2 that is rotationally driven is uniformly charged by the charger 3. Next, the optical system 4 irradiates a laser beam according to the image data to form an electrostatic latent image on the photosensitive drum 2. The toner of the developing device 5 adheres to the electrostatic latent image. On the other hand, cut paper is placed in the paper feed cassette 11, and the cut paper is picked up one by one by the pickup roller 12, and then sent to the transfer portion of the photosensitive drum 2 by the paper feed roller 13. The toner adhered to the photosensitive drum 2 is transferred to cut paper by the transfer charger 6 and fixed by the fixing device 12. The cut sheet after the fixing step is discharged to the discharge rollers 14,
The paper 16 is discharged to the paper discharge tray 13 via the paper discharge passage 15. The toner that has not adhered to the cut sheet is collected by the cleaner 8, and one printing operation is completed.

Next, the operation of the image reading section 20 will be described. Reading of the transmission original is performed in the same manner as in the conventional apparatus. That is, the originals placed on the original tray 21 are detected by the original sensor 22, and the originals are fed one by one to the position of the sensor 25 by the rollers 23. Next, the original is read by the contact linear image sensor 26 in synchronization with the rotation of the roller 24 by the motor (not shown) and the reading of the contact linear image sensor 26,
After being converted into digital image data, the original image is output to the buffer memory 59 shown in FIG. After the image reading is completed, the original is discharged by the discharge roller 27 to the discharge tray 2.
It is discharged to 8.

As shown in FIG. 2, the facsimile apparatus includes an MPU 50 for controlling the entire facsimile apparatus, an HDLC analyzer 52 for performing a facsimile signal processing and a communication processing, and a modem 53, respectively.
, An NCU 54, an image compression image memory 51 for temporarily storing a facsimile image signal, a buffer memory 59 and a page memory 61, and a compression / decompression unit 60 and an image restoration processing unit 62 for processing predetermined image signals, respectively. And each of the processing units 20, 51, 52, 5
3, 54, 59, 60 and 61 are connected to the MPU via the bus 63.
50. Also, the operation panel 40 is directly
The MPU 50 is connected to the MPU 50 and connected to the U50. The printer control unit 55 is provided in the printer unit 1 and controls the multivalued laser printer 70 that prints an image of the image data based on the multivalued image data.

First, the receiving operation of the facsimile apparatus will be described. When there is an incoming call from the other party's facsimile machine via the telephone line, the incoming call signal is sent to the NCU 54 and the modem 53.
After the data is input to the MPU 50 and detected, a line connection process with the destination facsimile device is executed in accordance with a predetermined facsimile line connection procedure. After the line connection processing, the compressed image signal transmitted from the other party's facsimile apparatus is input to the modem 53 via the NCU 54 and demodulated, and the demodulated compressed image data is compressed from the HDLC frame by the HDLC analysis unit 52. After performing a predetermined HDLC reverse processing for extracting only image data, the image data is stored in the compression image memory 51. When the compressed image signals of all pages are received, a line disconnection process with the destination facsimile apparatus is executed in accordance with a predetermined facsimile line disconnection procedure. The image data stored in the compression image memory 51 is expanded by the compression and expansion unit 60 into actual image data for each page using the page memory 61 and expanded. The image data expanded in the page memory 61 is input to an image restoration processing unit 62, converted into high-density binary image data by processing described later in detail, and then output to a printer control unit 55. In synchronization with the transfer of the image data to the printer control unit 55, the MPU 50
Outputs a recording start signal to the printer control unit 55,
The printer control unit 55 transmits a control signal and image data to the laser printer 70 to execute recording of the image data.

Next, the transmission operation of the facsimile machine will be described. When all of the above image reading operations by the image reading unit 20 are completed, a line connection process with the facsimile machine of the other party is executed. After the completion of the line connection processing, the compressed image data stored in the compression image memory 51 is temporarily stored in the page memory 61 by the compression / expansion unit 60.
After that, recompression processing is executed in accordance with the capability of the facsimile machine of the other party, and is stored in the compression image memory 51. The stored image data is sent to the HDLC analysis unit 5
After a predetermined HDLC frame processing process is performed by the modem 2, the signal is modulated by the modem 53 into a predetermined facsimile signal. The facsimile signal modulated with the image data is transmitted to the destination facsimile apparatus via the NCU 54 and the telephone line. When the transmission of the image data is completed, a line disconnection process with the destination facsimile apparatus is executed according to a predetermined line disconnection procedure, and the transmission operation ends.

The MPU 50 performs predetermined processing based on an operator's command input using the operation panel 40, and outputs instruction information to the operator and status information of the facsimile apparatus to the operation panel 40. indicate.

(3) Image Restoration Processing Unit As shown in FIG. 3, the image restoration processing unit 62 includes a halftone image restoration unit 101 for restoring multi-value halftone data from the received binary image data. However, this halftone image restoration processing has the following effects. That is, halftone image data such as a photographic image is generally represented by multi-valued image data having a plurality of bits per pixel. However, during communication of image data such as facsimile communication or storage of image data such as filing, the multi-valued image data is binarized by pseudo-halftone and converted into binary image data, so that communication or storage is performed. It is possible to greatly reduce the amount of data to be performed.

This halftone image restoration process is effective when, for example, halftone data binarized by pseudo halftone is recorded or displayed in binary at different pixel densities. In other words, the moiré due to the periodicity of the original pseudo halftone binary image data can be prevented by performing the scaling process after restoring the multi-valued image data once without performing the simple scaling process. . The restored multi-valued image data is binarized in pseudo halftone and output to an output system such as a display or a printer. At this time, if the output system can process input data at a high density, the characteristics of the device can be fully utilized. Further, for example, the halftone image restoration processing is performed when restoring binary image data binarized by pseudo halftone into multivalued image data and outputting it to an output system such as a multivalued display or a printer. It is valid.

FIG. 3 shows the image restoration processing unit 6 shown in FIG.
2 is a block diagram of FIG.

As shown in FIG. 3, binary image data read serially from the page memory 61 is 10 × 21
It is input to the matrix memory circuit 100. 10x2
The one-matrix memory circuit 100 includes, as shown in FIG. 30, pixel data D000 to D92 located at respective positions of a matrix in a 10 × 21 window W1021.
0 in the halftone image restoration unit 101 and the adjacent state detection unit 105 in the image area determination unit 102.
And the dither determination unit 106 and the simple multi-value conversion unit 103. In FIG. 30, an arrow MS indicates a main scanning direction, and an arrow SS indicates a sub-scanning direction. I is the window W1
021 is a parameter indicating the position of the main scanning line, and j
Is a parameter indicating the position of the sub-scanning line.

The halftone image restoring unit 101 includes a counting unit 1 in a window.
13, the edge area determining unit 109, and the smoothing amount calculating unit 11
0, an edge enhancement amount calculation unit 111, and a restored data calculation unit 112. The in-window counting unit 113 counts the number of black pixels in a plurality of predetermined windows and outputs data to the edge area determination unit 109, the smoothing amount calculation unit 110, and the edge enhancement amount calculation unit 111, respectively. The edge area determination unit 109 calculates an edge area determination amount used to determine an edge area based on the data output from the in-window counting unit 113 and outputs the calculated amount to the restored data calculation unit 112. Smoothing amount calculator 1
10 calculates a smoothing amount (7 × 7 black pixel number second data and 9 × 9 black pixel number data) for restoring halftone image data based on the data output from the intra-window counting section 113. Output to the restored data calculation unit 112. The edge enhancement amount calculator 111 performs an edge enhancement amount first to perform an edge enhancement process based on the data output from the in-window counting unit 113.
The data and the edge enhancement amount second data are calculated and output to the restored data calculation unit 112. Further, the restoration data calculation unit 1
Reference numeral 12 denotes multi-valued halftone image data based on the data output from the units 109 to 111 and the concentrated first dither image determination signal and the concentrated second dither image determination signal output from the determination data signal generation unit 114. Is restored and output to the data mixing unit 104.

The image area discriminating section 102 includes the adjacent state detecting section 10
5, a dither determination unit 106, a 5 × 11 matrix memory circuit 107, a determination data generation unit 108, and a determination data signal generation unit 114. Adjacent state determination unit 10
5 calculates the number of adjacent pixels in the main / sub scanning direction indicating the adjacent state of the smaller number of the same kind of pixels in the predetermined area in four main / sub scanning directions based on the input pixel data. , And calculates black pixel number data in a predetermined 3 × 3 window. Based on the calculated data and the black pixel number data in a predetermined 7 × 7 window output from the dither determination unit 106, An all-white / all-black image detection signal indicating whether the inside of the 7 × 7 window is an all-white image or an all-black image, and an image in a predetermined area centered on the pixel of interest being a halftone image And a non-halftone image detection signal indicating that the image in the predetermined area is a non-halftone image. On the other hand, based on the input pixel data, the dither determination unit 106 detects whether or not each pixel is a centralized systematic dither image having a screen angle of 0 degrees and indicates the detection result. In addition to outputting the first dither detection signal, it detects whether or not the image is a concentrated systematic dither image having a screen angle of 45 degrees and outputs a concentrated second dither detection signal indicating the detection result.

Further, the 5 × 11 matrix memory circuit 107 includes an adjacent state determination unit 105 and a dither determination unit 106
From the five detection signals (total 5 bits) serially output for each pixel from a predetermined 5 × 1
The data is simultaneously output to the determination data generation unit 108 within one window. The determination data generation unit 108 generates each determination data obtained by adding each detection signal within the 5 × 11 window for each detection signal based on each detection signal output from the matrix memory circuit 107. And outputs it to the discrimination data signal generator 114. Finally, the discrimination data signal generation unit 114 converts the image of the predetermined 5 × 11 window area into a centralized tissue having a screen angle of 0 degrees based on each judgment data output from the judgment data generation unit 108. Generating and outputting a centralized first dither image discrimination signal indicating a discrimination result by discriminating whether or not the image is a dynamic dither image;
It is determined whether or not the image of the predetermined 5 × 11 window area is a concentrated systematic dither image having a screen angle of 45 degrees, and a concentrated second dither image determination signal indicating a determination result is generated. And outputs image area discrimination data indicating the result of discriminating between a halftone area and a non-halftone area. Here, the image area determination data has a value range from 0 to 1, which is 0 when the image in the area is completely a halftone image, and is 1 when the image is a non-halftone image.

Further, based on the pixel data output from the matrix memory circuit 100, the simple multi-level conversion unit 103 uses a predetermined threshold value to perform non-halftone binarization using a predetermined threshold value.
The value image data is simply converted to multi-valued non-halftone image data indicating white or black and output to the data mixing unit 104 as non-halftone data. Further, the data mixing unit 104 is configured by a table ROM, and stores the multi-value halftone image data output from the halftone image
Based on the multi-valued non-halftone image data output from No. 03 and the image area discrimination data, the following Expression 1 is calculated,
That is, these data are mixed in accordance with the mixing ratio indicated by the image area determination data, thereby generating 6-bit multi-valued image data and outputting it to the printer control unit 55.

## EQU1 ## Multi-valued image data = (halftone image data) × {1- (image area discrimination data)} + (non-halftone image data) × (image area discrimination data)

In this embodiment, in order to make the erroneous determination of the image area distinction in a region where both halftones and non-halftones can be obtained inconspicuous,
As described above, the data mixing unit 104 mixes the halftone image data and the non-halftone image data according to the mixing ratio of the image area discrimination data indicating the halftone image likeness and the non-halftone image likeness. The value image data is restored.

(4) 10 × 21 Matrix Memory Circuit FIG. 4 is a block diagram of the 10 × 21 matrix memory circuit 100 shown in FIG. As shown in FIG.
The 0 × 21 matrix memory circuit 100 receives an image input based on a clock CLK having the same cycle as the transfer clock cycle of the binary image data input from the page memory 61, that is, one dot cycle of the image data. Nine FIFO memories DM1 to DM9 for delaying data by one horizontal period, which is one scanning time in the main scanning direction, and image data input in synchronization with the clock CLK for one cycle period of the clock CLK. 200 delayed flip-flops D that are delayed and output
F001 to DF020, DF101 to DF120,
DF201 to DF220,. . . , DF901 through D
F920.

The binary image data serially output from the page memory 61 in the direction from the first pixel to the last pixel of the image of each page is input to the flip-flop DF001 and then cascade-connected. After being output through the flip-flops DF001 to DF020 and input to the FIFO memory DM1, the data is output through nine cascade-connected FIFO memories DM1 to DM9. After the image data output from the FIFO memory DM1 is input to the flip-flop DF101,
Cascaded flip-flops DF101 to DF1
20 is output. Also, the FIFO memory DM2
Is output from the flip-flop DF2
01, and then output through cascaded flip-flops DF201 to DF220. Less than,
Similarly, the image data output from each of the FIFO memories DM3 to DM9 is respectively
After being input to 301 to DF901, cascade-connected flip-flops DF301 to DF320,
DF401 to DF420,. . . , DF901 through D
Output via F920.

In the 10 × 21 matrix memory circuit 100 configured as described above, when 1-dot pixel data first input to the circuit 100 is output from the flip-flop DF920, the image input at that time is output. The data is output as pixel data D000, and each flip-flop DF001 to DF020 is output.
Outputs pixel data D001 to D020 on the main scanning line of i = 0 in a 10 × 21 window, respectively, and outputs the FIFO memory DM1 and each flip-flop DF.
Each of the pixel data D100 to D120 on the main scanning line of i = 1 in the 10 × 21 window is output from 101 to DF120, and each of the pixel data D100 to D120 in the 10 × 21 window is output from the FIFO memory DM2 and each of the flip-flops DF201 to DF220. The pixel data D200 to D220 on the main scanning line of = 2 are output. Similarly, the pixel data D300 to D920 are output from the FIFO memories DM3 to DM9 and the flip-flops DF301 to DF920, respectively.

In FIG. 31, WP is a reference range of each pixel at the time of judgment by the adjacent state judging unit 105 and the dither judging unit 106. Indicated by. Also, W51
Reference numeral 1 denotes a reference range of each pixel at the time of determination in the determination data signal generation unit 114, and a target pixel (i = 5, j = 5) at the time of the determination is indicated by ◎. Here, the reference range W511 is a 5 × 11 window centered on the target pixel ◎. Further, W9 is a reference range of each pixel at the time of restoration by the halftone image restoration unit 101, and the target pixel (i = 5, j = 5) at the time of the restoration processing is indicated by ◎. Here, the reference range W9 is 9 around the target pixel ◎.
This is a × 9 window.

(5) Image Area Determining Section (5-1) Configuration and Operation of Each Section FIGS. 5 to 19 are block diagrams of the image area determining section 102 shown in FIG. An adjacent state determination unit 105, a dither determination unit 106, a 5 × 11 matrix memory circuit 107, a determination data generation unit 108,
A determination data signal generation unit 114; Hereinafter, features of the processing in the image area determination unit 102 will be described.

FIG. 32 shows an example of a non-halftone image obtained when a character image is read and then binarized using a predetermined threshold value. FIG. An example of a pseudo halftone binarized image obtained when binarization is performed by an error diffusion method is shown. FIG. 34 shows an example of a pseudo halftone binarized image obtained when a photographic image is read and then binarized by a dot concentration type systematic dither method with a screen angle of 0 degrees. An example of a pseudo halftone binary image obtained when a photographic image is read and then binarized by a dot concentration type systematic dither method with a screen angle of 45 degrees. In the following, for convenience of explanation, the image shown in FIG. 33 is called a dispersed halftone image, and the images shown in FIGS. 34 and 35 are called a concentrated halftone image. The image in FIG. 34 is called a concentrated first dither image, and the image in FIG. 35 is called a concentrated second dither image.

In this embodiment, a process for determining whether or not the image of the input image data is a distributed halftone image is performed by the adjacent state determination unit 105. Processing for determining whether or not the image is a concentrated halftone image is performed by the dither determination unit 106.
In addition, the adjacent state determination unit 105 compares the image of FIG.
Of the image is determined. In each window W7 in FIGS. 32 and 33, the same number of black pixels are present, and it can be said that the image density of each image in the window W7 is the same. The major difference between the images in the window W7 is the main scanning direction and the sub-scanning direction of a small number of pixels (hereinafter, referred to as the main-sub-scanning direction).
Are adjacent states. Here, the minority pixel refers to a pixel having a smaller number among white pixels and black pixels in a predetermined window. In the examples of FIGS. 32 and 33, the minority pixel is a black pixel.

The total number of minority pixels connected by the same kind of pixels as the minority pixel in any of the four directions from the minority pixel to the main / sub scanning direction is hereinafter referred to as the number of adjacent pixels in the four directions.
In general, in a graph of the number of black pixels in a 7 × 7 window and the number of adjacent pixels in the four main and sub-scanning directions, the halftone image region and the non-halftone image region are divided and shown as in FIG. As is clear from FIG. 36, when the number of black pixels and the number of white pixels are equal within a predetermined window, the number of adjacent pixels in the four directions indicating the threshold on the boundary line of each image region increases, and the threshold value increases. A non-halftone image exists when the number of adjacencies in four directions is larger than the value, and a non-halftone image exists when the number of adjacencies in four directions is smaller than the threshold value. Therefore, in this embodiment, the threshold data is stored in the table ROM 156 shown in FIG. 16, and the number of neighbors in four directions, that is, the number of neighbors in the main / sub scanning direction, is output from the table ROM 156 by the comparator 157. Each of the image regions is determined by comparing the threshold value with the threshold value.

Further, in this embodiment, the distinction of the intensive halftone image is performed using the periodicity of the change of the image density in the main scanning direction or the sub-scanning direction, that is, the peripheral distribution characteristic. The number of types of dot-intensive systematic dithering is theoretically infinite, but in reality, only a limited number of types of dithering are practical due to the number of gradations of the image reading device and the resolution of the image recording device. is not. Therefore, in the present embodiment, the centralized first dither image of FIG. 34 and the centralized second dither image of FIG. 35 are determined.

In order to determine the former concentrated-type first dither image, eight windows shown in FIGS. 37 and 38 are used. In FIGS. 37 and 38 and the following figures, * indicates a target pixel. As shown in FIG.
The count value data of the number of black pixels in the seven pieces of pixel data D007 to D607 that are continuous in the sub-scanning direction is data S10, and the seven pieces of pixel data D that are continuous in the sub-scanning direction are data S10.
The count value data of the number of black pixels in 008 to D608 is referred to as data S11, and similarly, data S12 to S1
7 is defined. Further, as shown in FIG. 38, seven pieces of pixel data D007 to D013 continuous in the main scanning direction.
The count value data of the number of black pixels within the pixel data is referred to as data S20.
To D113, the count value data of the number of black pixels
1, data S22 to S27 are defined in the same manner. The peripheral distribution of the number of black pixels counted in the main scanning direction or the sub-scanning direction of these data is generally shown in FIG.
It becomes as shown in. In FIG. 41, X is a number of 1 or 2.

In order to determine the latter concentrated type second dither image, eight windows shown in FIGS. 39 and 40 are used. As shown in FIG. 39, seven continuous oblique directions from the upper right to the lower left (hereinafter, referred to as a first oblique direction) that are inclined at an angle of 45 degrees with respect to both the main scanning direction and the sub scanning direction. Each pixel data D006 to D6
The count value data of the number of black pixels in 00 is data S30,
Also, seven pieces of pixel data D continuous in the first diagonal direction are provided.
The count value data of the number of black pixels in 008 to D602 is referred to as data S31, and similarly, data S32 to S3
7 is defined. Further, as shown in FIG. 40, the direction is continuous in an oblique direction from the upper left to the lower right (hereinafter, referred to as a second oblique direction) which is obliquely inclined by 45 degrees with respect to both the main scanning direction and the sub scanning direction. The count value data of the number of black pixels in each of the seven pieces of pixel data D000 to D606 is converted to data S40.
In addition, the count value data of the number of black pixels in each of the seven pieces of pixel data D002 to D608 that are continuous in the second oblique direction is defined as data S41, and data S42 to S47 are defined in the same manner. The determination of the concentrated second dither image is also performed based on the peripheral distribution characteristics, similarly to the above-described determination of the concentrated first dither image.

(5-2) Dither Determination Unit The dither determination unit 106 shown in FIG. 3 includes a first calculation unit 106a in FIG. 5, a second calculation unit 106b in FIG. 6, and a third calculation unit 106c in FIG. 9, the fourth calculating unit 106d in FIG. 9, the fifth calculating unit 106e in FIG. 10, and the sixth calculating unit 10 in FIG.
6f, and a seventh calculating unit 106g of FIG. In addition,
In the drawings after FIG. 5, the number of bits is displayed in the vicinity of the symbol of each data bus, and in the display of the number of bits, for example, 7 (S1) indicates that 7 (S1) includes one sign bit. Indicates a bit data bus.

FIG. 5 shows the first calculation unit 1 of the dither judgment unit 106.
FIG. 6 is a block diagram of the dither determination unit 106a.
FIG. 10 is a block diagram of a second calculation unit 106b. These first
And 32 logic Bs that output 3-bit data indicating the number of black pixels included in the pixel data based on the 7-bit pixel data in the second calculation units 106a and 106b.
Circuits LB-10 to LB-17, LB-20 to LB-
27, LB-30 to LB-37, LB-40 to LB
−47, data S10 to S17, S
20 to S27, S30 to S37, S40 to S47
Generate

FIG. 7 is a block diagram of the logic B circuit LB shown in FIG. 5 and FIG.
The logic B circuit LB shown in FIGS.

As shown in FIG. 7, the logic B circuit LB performs a predetermined logic B operation on the input 7-bit data D1 to D7, and then performs the logic "1" of the input 7-bit data. A logic circuit that outputs 3-bit data Q1, Q2, and Q3 of a calculation result indicating the number of bits of a "(black pixel). Hereafter, two logic A circuits LA-1 and LA-2 for performing a logic A operation) and one adder AD1 are provided.

[0047]

(Equation 2)

(Equation 3)

The data D1 to D3 of the least significant three bits are input to the logic A circuit LA-1, and the data D1 to D3
Are input to the logic A circuit LA-2, the most significant 1-bit data D7 is input to the carry-in terminal CI of the adder AD1, and each of the logic A circuits LA-1,. LA-2 performs the above-described logical A operation on the input 3-bit data, and then calculates 2
The bit data is output to the adder AD1. Adder AD
1 adds the input two 2-bit data,
It outputs 3-bit data Q1, Q2, Q3 of the addition result.

FIG. 8 is a block diagram of the third calculating section 106c of the dither determining section 106. As shown in FIG. 8, the data S10 to S16 are added by the adders AD11 to AD16, and the 7 × 7 black pixel number first data, which is the addition result, is sent from the adder AD16 to the input terminal A of the comparator CM1. The signals are output to the input terminals A of the devices AD17 and AD18 and to the third calculation unit 105c of the adjacent state determination unit 105 in FIG. The adder AD17 adds the 7 × 7 black pixel number first data and the data S17, and outputs the addition result data S1A to the sixth calculation unit 106f of the dither determination unit 106 in FIG. Further, the adder AD18 has the first 7 × 7 black pixel number.
The data and the data S20 are added, and the data S
2A is calculated by the sixth calculating unit 106 of the dither determining unit 106 in FIG.
Output to f. Further, the comparator CM1 compares the calculated 7 × 7 black pixel number first data with “24” data, and when the 7 × 7 black pixel number first data is larger than “24”, the minority pixel Is output to the first calculation unit 105a of the adjacent state determination unit 105, indicating that the pixel is a white pixel, while the 7 × 7 black pixel number first data is equal to “24” or When the value is small, a low-level black-and-white selection signal SEL1 indicating that a small number of pixels are black pixels is similarly output.

FIG. 9 is a block diagram of the fourth calculating unit 106 d of the dither determining unit 106. As shown in FIG. 9, the data S30 to S37 are added by the adders AD21 to AD27, and the addition result data S3A is added to the adder AD2.
7 to the seventh calculation unit 106 g of the dither determination unit 106.

FIG. 10 is a block diagram of the fifth calculating unit 106e of the dither determining unit 106. As shown in FIG.
Data S40 to S47 are the adders AD31 to AD37.
And the data S4A of the addition result is added to the adder A
D37 is output to the seventh calculation unit 106g of the dither determination unit 106.

FIG. 11 is a block diagram of the sixth calculating unit 106f of the dither determining unit 106.

As shown in FIG. 11, data S10 to S10
17 are respectively input to the multipliers MU10 to MU17, and after each data is multiplied by eight, each data of the multiplication result is output to each input terminal A of each of the comparators CM10 to CM17. On the other hand, the data S1A is output from the comparators CM10 to C10.
Each of the comparators CM10 to CM17 compares the data input to each of the input terminals A and B. When A> B, the comparator CM10 to CM17 outputs an H level signal to the centralized first dither. ROMRT for table for determining images
1, and outputs an L-level signal when A ≦ B. ROMRT1
Outputs an H-level signal to the first input terminal of the AND gate AND1 when determining that the image is a concentrated first dither image, and outputs an L-level signal when determining that the image is not a concentrated first dither image. Output in the same way.

The data S20 to S27 are input to multipliers MU20 to MU27, respectively, and each data is
After the multiplication, each data of the multiplication result is output to the comparator CM, respectively.
The signals are output to the input terminals A of the CMs 20 to 27. on the other hand,
The data S2A is input to the input terminals B of the comparators CM20 to CM27, and the comparators CM20 to CM27 compare the data input to the input terminals A and B. When A> B, an H level signal is output. Is output to each of the address terminals A7 to A0 of the table ROM RT2 for determining the centralized first dither image. When A ≦ B, an L-level signal is similarly output. The ROMRT2 outputs an H-level signal to the second input terminal of the AND gate AND1 when determining that the image is the centralized first dither image, and outputs an L-level signal when determining that the image is not the centralized first dither image. Is similarly output.

Further, the AND gate AND1 outputs the centralized first dither detection signal indicating the result of the logical product of the input signals to the 5 × 11 matrix memory circuit 107 in FIG. 17 as one of the determination data.

In the sixth calculating section 106f, the magnitude relation between the number of black pixels in each window shown in FIGS. 38 and 39 and their average number of black pixels is determined by comparators CM10 to CM1.
7. As described above with reference to the peripheral distribution characteristics shown in FIG. 41, the data indicating the magnitude relation is obtained from the table ROMs RT1 and RT2 to obtain the data in the main scanning direction. It is determined whether the image is a concentrated first dither image in both sub-scanning directions. Next, an H-level centralized first data detection signal is output only when it is determined that the image is a centralized first dither image in both directions.

FIG. 12 is a block diagram of the seventh calculating unit 106g of the dither determining unit 106.

As shown in FIG. 12, data S30 through S30
37 are input to the multipliers MU30 to MU37, respectively, and after multiplying each data by 8, the respective data of the multiplication result are output to the input terminals A of the comparators CM30 to CM37, respectively. On the other hand, the data S3A indicates that the comparators CM30 to CM30C
Each of the comparators CM30 to CM37 compares the data input to each of the input terminals A and B, and outputs an H-level signal when A> B, and outputs the H-level signal to the centralized second dither. ROMRT for table for determining images
3 and outputs an L-level signal when A ≦ B. ROMRT3
Outputs an H level signal to the first input terminal of the AND gate AND2 when it is determined that the image is a concentrated second dither image, and outputs an L level signal when it is determined that the image is not a concentrated second dither image. Output in the same way.

The data S40 to S47 are input to the multipliers MU40 to MU47, respectively.
After the multiplication, each data of the multiplication result is output to the comparator CM, respectively.
The signals are output to the input terminals A of the CMs 40 to 47. on the other hand,
The data S4A is input to the input terminals B of the comparators CM40 to CM47. The comparators CM40 to CM47 compare the data input to the input terminals A and B. When A> B, an H level signal is output. Is output to each address terminal A7 to A0 of the table ROM RT4 for determining the concentrated second dither image, and when A ≦ B, an L level signal is similarly output. The ROMRT4 outputs an H-level signal to the second input terminal of the AND gate AND2 when determining that the image is the concentrated second dither image, and outputs an L-level signal when determining that the image is not the concentrated second dither image. Is similarly output.

Further, the AND gate AND2 outputs the centralized second dither detection signal indicating the result of the logical product of the input signals to the 5 × 11 matrix memory circuit 107 in FIG. 17 as one of the judgment data.

The seventh calculating section 106g determines the magnitude relationship between the number of black pixels in each window shown in FIGS. 40 and 41 and the average number of black pixels by the comparators CM30 to CM3.
7, as described above with reference to the marginal distribution characteristics shown in FIG. 41 and by referring to the peripheral distribution characteristics shown in FIG. It is determined whether the image is a concentrated type second dither image in both the direction and the second oblique direction. Next, an H-level centralized second data detection signal is output only when it is determined that the image is a centralized second dither image in both directions.

(5-3) Adjacent State Determination Unit The adjacent state determination unit 105 shown in FIG.
It includes a calculation unit 105a, a second calculation unit 105b in FIG. 15, and a third calculation unit 106c in FIG.

In this embodiment, the number of adjacent small pixels in the four main scanning directions is determined by counting the number of adjacent pixels indicated by arrows in FIGS. 42 and 43. Here, in order to count the number of adjacencies on each scanning line in the main scanning direction or the sub-scanning direction, an adjacency number counter (hereinafter, referred to as an adjacency number counter) CA shown in FIG.
Is used.

FIG. 13 is a block diagram of the first calculation unit 105a of the adjacent state determination unit 105 shown in FIG. FIG.
As shown in FIG. 3, each of the adjacent number counters CA-1 to CA-
7. A black-and-white selection signal SEL1 is supplied to CA-11 to CA-17.
Is entered.

Seven pixel data D continuous in the main scanning direction
007 to D013 are input to the adjacent number counter CA-1, the adjacent number of the small number of pixels is counted, and then output to the adder AD41.
07 to D113 are input to the adjacent number counter CA-2, the number of adjacent small pixels is counted, and then output to the adder AD41.
7 to D213 are input to the adjacent number counter CA-3, and the number of adjacent small pixels is counted.
To D313 are input to the adjacent number counter CA-4, and the adjacent numbers of the small number of pixels are counted and then output to the adder AD42. Also, seven pixel data D continuous in the main scanning direction
407 to D413 are input to an adjacent number counter CA-5, the adjacent number of a small number of pixels is counted, and then output to an adder AD44.
07 to D513 are input to an adjacent number counter CA-6, and the number of adjacent small pixels is counted.
7 to D613 are input to the adjacent number counter CA-7, the number of adjacent small pixels is counted, and then output to the adder AD45.

The seven consecutive pixel data D007 to D607 in the sub-scanning direction are stored in the adjacent number counter CA-11.
Is added to the adder A after the number of adjacent pixels is counted.
D45, and the seven consecutive pixel data D008 to D608 in the sub-scanning direction are output to the adjacent number counter CA-12.
Is added to the adder A after the number of adjacent pixels is counted.
D51 and seven consecutive pixel data D009 to D609 in the sub-scanning direction are output to the adjacent number counter CA-13.
Is added to the adder A after the number of adjacent pixels is counted.
D51 and seven consecutive pixel data D010 to D610 in the sub-scanning direction are output to the adjacent number counter CA-14.
Is added to the adder A after the number of adjacent pixels is counted.
Output to D52. Furthermore, 7 continuous in the sub-scanning direction
The pixel data D011 to D611 are input to the adjacent number counter CA-15, the number of adjacent small pixels is counted, and then output to the adder AD52.
The pixel data D012 to D612 are input to the adjacent number counter CA-16, the adjacent number of the small number of pixels is counted, and then output to the adder AD54.
The pieces of pixel data D013 to D613 are input to an adjacent number counter CA-17, and the number of adjacent small pixels is counted, and then output to the adder AD54.

Each of the adjacent number counters CA-1 to CA-
The number of neighbors of each small pixel counted by 7 to CA-11 to CA-17 is calculated by adders AD41 to AD47, A
After the addition by D51 to AD56, the number of neighbors in the main and sub-scanning directions resulting from the addition is output from the adder AD56 to the third calculation unit 105c of the adjacent state determination unit 105 in FIG.

FIG. 14 is a block diagram of each adjacent number counter CA shown in FIG.

As shown in FIG. 14, the first bit data D1 of the input 7-bit data is supplied to the first input terminal of the AND gate AND11 and the NOR gate NOR1.
, And the second bit data D2 is input to the second input terminal of the AND gate AND11, the second inverted input terminal of the NOR gate NOR12, the first input terminal of the AND gate AND13, and NOR gate NOR
It is input to fourteen first inverting input terminals. Also, the third bit data D3 is a second input terminal of the AND gate AND13, a second inverting input terminal of the NOR gate NOR14,
The signal is input to the first input terminal of the AND gate AND15 and the first inverting input terminal of the NOR gate NOR16,
The bit data D4 is input to a second input terminal of the AND gate AND15, a second inverted input terminal of the NOR gate NOR16, a first input terminal of the AND gate AND17, and a first inverted input terminal of the NOR gate NOR18. Further, the fifth bit data D5 is provided by AND gate A
The second input terminal of the ND17, the second inverting input terminal of the NOR gate NOR18, the first input terminal of the AND gate AND9, and the first inverting input terminal of the NOR gate NOR20 are input to the sixth input terminal. Gate A
The signal is input to a second input terminal of the ND19, a second inverted input terminal of the NOR gate NOR20, a first input terminal of the AND gate AND21, and a first inverted input terminal of the NOR gate NOR22. Furthermore, the seventh bit data D7
Is input to the second input terminal of the AND gate AND21 and the second inverting input terminal of the NOR gate NOR22.

Each of the logic gates AND11 to AND21,
Signals output from NOR12 to NOR22 are input terminals A1, B1, A2,
B2, A3, B3, A4, B4, A5, B5, A6, B
6, the selector SE11 selects data input to each of the input terminals A1 to A6 in order to count the number of adjacent black pixels, which are a small number of pixels, when the L-level black-and-white selection signal SEL1 is input. Each output terminal Y1
To Y3 to the respective input terminals of the logic A circuit LA-11, and from the respective output terminals Y4 to Y6 to the respective input terminals of the logic A circuit LA-12. On the other hand, the H level monochrome selection signal SEL1 is input. In order to count the number of adjacent white pixels, which are a small number of pixels, the input terminals B1 to B1
6 are selected and output similarly. Each 2-bit data output from each of the logic A circuits LA-11 and LA-12 is input to the adder AD57, and after being added by the adder AD57, the data of the addition result is counted as the adjacent number in the main-sub scanning direction. Output as a number.

FIG. 15 is a block diagram of the second calculating section 105b of the adjacent state judging section 105.
Reference numeral 5b denotes a circuit for counting the number of black pixels in a 3 × 3 window centered on the pixel data D310.

As shown in FIG. 15, three data D209 to D211 continuous in the main scanning direction are logical A circuit LA.
-13, the number of black pixels in each data is counted, and the data of the counted value is output to the adder AD58. Also, three data D309 continuous in the main scanning direction
To D311 are input to the logic A circuit LA-14, and after the number of black pixels in each data is counted, the data of the count value is output to the adder AD58. Further, three consecutive data D409 to D411 in the main scanning direction are input to the logic A circuit LA-15, and after the number of black pixels in each data is counted, the data of the counted value is output to the adder AD59. Is done. Each data counted by each of the logic A circuits LA-13 to LA-15 is added by the adders AD58 and AD59, and the data of the addition result is sent from the adder AD59 to the third calculation unit 105c in FIG. It is output as pixel number data.

FIG. 16 is a block diagram of the third calculation unit 105c of the adjacent state determination unit 105.

As shown in FIG. 16, 3 × 3 black pixel number data is input to input terminals B of comparators 150 and 151,
On the other hand, data “0” is input to the input terminal A of the comparator 150, and data “9” is input to the input terminal A of the comparator 151. Comparator 150 outputs an H-level signal to the first input terminal of AND gate 158 only when A = B, and similarly outputs an L-level signal otherwise. The comparator 151 outputs an H level signal to the first input terminal of the AND gate 159 only when A = B, and outputs an L level signal otherwise.

The third calculating section 106c of the dither determining section 106
Is output from each comparator 1
While being input to each of the input terminals B of 52 to 155,
Table ROM 156 storing a threshold value table for determining whether it is a halftone image area or a non-halftone area
Is input to the address terminal. Threshold data TJ0, which will be described in detail later, is input to the input terminal A of the comparator 152,
The data of “49-TJ0” is input terminal A of the comparator 153.
Is input to The comparator 152 outputs an H-level signal to the second input terminal of the AND gate 158 when A> B,
On the other hand, otherwise, an L-level signal is output similarly. The comparator 153 outputs an H-level signal to the second input terminal of the AND gate 159 when A <B, and similarly outputs an L-level signal otherwise.
Each output signal from the AND gates 158 and 159 is input to the NOR gate 160, and the output signal of the NOR gate 160 is input to each first input terminal of the AND gates 162 and 163.

The data “0” is input to the input terminal A of the comparator 154, and the data “49” is input to the comparator 155.
Is input to the input terminal A. The comparator 154 outputs an H-level signal to the first input terminal of the OR gate 161 when A = B, and outputs an L-level signal otherwise. Further, when A = B, the comparator 155
The H-level signal is output to the second input terminal of the OR gate 161, and otherwise, the L-level signal is output similarly. The OR gate 161 converts the signal indicating the result of the logical sum of the input signals into a 5 × 7 × 7 × 7 × 7 × 7 × 7 × 7 × 5 × 5 ×××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××××× Output to the 11 matrix memory circuit 107.

The threshold data output from the table ROM 156 is input to the input terminal A of the comparator 157.
On the other hand, the number of adjacencies in the main and sub scanning directions is equal to the input terminal B of the comparator
Is input to As described above with reference to FIG.
A comparator 1 provided to determine whether the image of the area in the window is a halftone image or a non-halftone image
57 is an AND gate 1 which outputs a signal of H level when A> B.
62, and outputs an H-level signal to the second input terminal of the AND gate 163 when A <B. The signal output from the AND gate 162 is output to the 5 × 11 matrix memory circuit 107 as a halftone image detection signal, and the signal output from the AND gate 163 is output to the 5 × 11 matrix memory as a non-halftone image detection signal. Output to the memory circuit 107.

In the third calculation unit 105c, 7 ×
7, based on the threshold data output from the table ROM 156 based on the first data of the number of black pixels in the window and the counted number of adjacent pixels in the main / sub scanning direction, the image of the area in the window is changed to a halftone image. Or a non-halftone image is determined by the comparator 157. However, in the following two cases, the determination result is “unknown”, and the image is neither a halftone image nor a non-halftone image. (A) When the number of neighbors in the main and sub scanning directions matches the threshold data, that is, when A = B in the comparator 157. (B) a small number of pixels are smaller than a predetermined threshold value TJ0 within the 7 × 7 window (when the output signal of the comparator 152 is at the H level or when the output signal of the comparator 153 is at the H level), Also, when a small number of pixels do not exist in the 3 × 3 window centered on the target pixel (when the output signal of the comparator 150 is at the H level or when the output signal of the comparator 151 is at the H level).

In order to determine “unknown” in the case of (a), the comparator 157 sends H to the AND gates 162 and 163.
It is configured so that a level signal is not input. In addition, in order to make “unknown” in the case of (b), a signal that becomes active at the L level and is output from the NOR gate 160 is input to the AND gates 162 and 163.

The reason for "unknown" in the case of (b) will be described below with reference to FIGS. 45 and 46. FIG.
5 and 46 are diagrams illustrating an example of a case where a linear non-halftone image approaches a 7 × 7 window W7. In the state shown in FIG. 45, only the end of the linear image enters the window W7, so that it cannot be distinguished from an isolated point. The image is determined to be a halftone image. Therefore, as shown in FIG. 46, it is better to set the determination result to “unknown” until the linear image approaches the periphery of the target pixel to some extent. This is performed depending on whether or not a small number of pixels exist in the window W3. Also, in this case, of course, the small number of pixels are equal to the threshold TJ0.
It should be less than that, so we make that decision at the same time.

Further, in the calculation unit 105c,
Whether all the pixels in the 7 × 7 window W7 are white pixels or black pixels is determined by the comparators 154 and 155. In either case, the all-white / all-black detection signal is set to the H level. The detection signal is transmitted to the discrimination data signal generation unit 11
4 is used to determine the concentrated dither image.

Further, the reason why the threshold value TJ0 is provided in the calculation unit 105c will be described below. For example, as shown in FIG. 44, when there are no small number of pixels around the target pixel *, but there are a relatively large number of small number of pixels in the 7 × 7 window W7 for adjacent determination, the halftone image / non-halftone It can be said that the image can be determined. In the case of FIG. 44, 7 × 7 windows 7
Although there are a small number of pixels, the image is naturally determined to be a non-halftone image. Therefore, as a condition that the determination result is “unknown”, (the number of small pixels in the 7 × 7 window W7 <TJ
0) and (there is no small number of pixels in the 3 × 3 window W3). The case where this conditional expression is satisfied is the following two cases. (A) When a small number of pixels are white pixels, each comparator 15
0, 152 when the output signal is at the H level. (B) When a small number of pixels are black pixels, each comparator 15
1,153 output signals are at H level. In this embodiment, a preferable value of the threshold value TJ0 is 6.

(5-4) 5 × 11 Matrix Memory Circuit FIG. 17 is a block diagram of the 5 × 11 matrix memory circuit 107 shown in FIG.

As shown in FIG. 17, the 5 × 11 matrix memory circuit 107 sets the same cycle as the cycle of the transfer clock of the binary image data input from the page memory 61, that is, the cycle of one dot of the image data. The 5-bit determination data composed of the following five detection signals detected and input for each pixel in synchronization with the clock CLK having the same is delayed by one horizontal period, which is one scanning time in the main scanning direction. FIFO memories DM11 to DM1
And 50 delay flip-flops DG301 to DG310 and DG401 to DG41 for delaying and outputting the 5-bit decision data, which are respectively input in synchronization with the clock CLK, by one cycle period of the clock CLK.
0, DG501 to DG510,. . . , DG701 to DG710. The respective circuits of the matrix memory circuit 107 process the following 5-bit determination data in parallel. (A) Sixth calculation unit 106 of dither determination unit 106 in FIG.
Centralized first dither detection signal output from f (hereinafter referred to as determination data A). (B) The sixth calculation unit 106 of the dither determination unit 106 in FIG.
Centralized second dither detection signal output from g (hereinafter referred to as determination data B). (C) Third calculation unit 10 of adjacent state determination unit 105 in FIG.
5c is a halftone image detection signal (hereinafter, referred to as determination data C). (D) Third calculation unit 10 of adjacent state determination unit 105 in FIG.
Non-halftone image detection signal (hereinafter, referred to as determination data D) output from 5c. (E) Third calculation unit 10 of adjacent state determination unit 105 in FIG.
5c is an all-white / all-black image detection signal (hereinafter, referred to as determination data E).

The 5-bit determination data serially output from the above-described calculation circuits in the direction from the first pixel to the last pixel of the image of each page is supplied to the flip-flop DG30.
1 and then output via ten cascaded flip-flops DG301 to DG310, and after being input to FIFO memory DM11, four cascaded FIFO memories DM11 to DM14.
Is output via. After the image data output from the FIFO memory DM11 is input to the flip-flop DG401, the cascade-connected flip-flop DG40
1 through DG410. Also, FIFO
The image data output from the memory DM12 is input to the flip-flop DG501 and then output via the cascade-connected flip-flops DG501 to DG510. Hereinafter, similarly, each FIFO memory DM13
After the determination data output from the DM14 to DM14 are input to the flip-flops DG601 to DG701, respectively, the flip-flops DG60 are connected in cascade.
1 through DG610 and DG701 through DG710.

In the 5 × 11 matrix memory circuit 107 configured as described above, the 5-bit decision data corresponding to the pixel data of one dot, which is input first to the circuit 107, is output from the flip-flop DG710. At this time, the judgment data input at that time is output as the judgment data J300, and 5 × 11
Judgment data J300 to J310 corresponding to each pixel data on the main scanning line of i = 3 in the window of
FIFO memory DM11 and each flip-flop DG4
01 to DG410, each pixel data J400 to J on the main scanning line of i = 4 in a 5 × 11 window
410 is output, and 5 are output from the FIFO memory DM12 and the flip-flops DG501 to DG510, respectively.
The respective pixel data J500 to J510 on the main scanning line of i = 5 in the window of × 11 are output. Similarly, the respective pixel data J600 to J600 are respectively output from the FIFO memories DM13 and DM14 and the flip-flops DG601 to DG710. J610, J700 to J71
0 is output.

Therefore, as shown in FIG. 47, each pixel (i = 3, 4,..., 7; j = 0,
The determination data A to E of 5 bits per pixel corresponding to (1, 2,..., 10) are simultaneously output from the matrix memory circuit 107 to the determination data counting unit 108 in FIG.

(5-5) Judgment Data Generation Unit FIG. 18 is a block diagram of the judgment data counting unit 108 shown in FIG. 3. In the present embodiment, the circuit shown in FIG. 5 to E are provided. In FIG. 18, X corresponds to A to E in the determination data A to E. For example, 5-bit determination data J300 is converted to J300-X (X = A, B,
C, D, E).

As shown in FIG. 18, judgment data A to E output from the matrix memory circuit 107 are divided into eight logic B circuits LB-51 to LB for each judgment data.
After the number of H-level (“1”) data is counted for each 7-bit determination data input to each of the circuits LB-51 to LB-58, the count data is added. Are added by the devices AD60 to AD66, and the data of the addition result is the determination data count value JS-X (X
= A, B, C, D, E). Therefore, the judgment data A to E are added for each judgment data in the window shown in FIG. 47, and the data of the addition result is output as the judgment data count value JS-X. Here, the judgment data count value JS-X (X = A, B, C, D, E)
Means the following data corresponding to the determination data A to E, and these five data are output to the determination data signal generation unit 114. (A) Judgment data count value JS-A of judgment data A in the window of FIG. 47: number of detected pixels of centralized first dither image. (B) The determination data count value JS-B of the determination data B in the window of FIG. 47: the number of pixels of the centralized second dither image. (C) Judgment data count value JS-C of the judgment data C in the window of FIG. 47: the number of halftone image detection pixels. (D) The judgment data count value JS-D of the judgment data D in the window of FIG. 47: the number of non-halftone image detection pixels. (E) Judgment data count value JS-E of judgment data E in the window of FIG. 47: the number of all white and all black image detection pixels.

(5-6) Discrimination Data Signal Generation Unit FIG. 19 shows the discrimination data signal generation unit 114 shown in FIG.
It is a block diagram of.

As shown in FIG. 19, the number of concentrated first data detection pixels JS-A is used for a table for discriminating a concentrated first dither image and outputting a non-halftone index and a discrimination signal for the image. The number of pixels JS-B, which is inputted to the first address terminal of the ROMRT7 and is the centralized second data detection pixel
Is input to the first address terminal of the table ROMRT6 for determining the centralized second dither image and outputting a non-halftone index and a determination signal for the image. The halftone image detection pixel number JS-C is input to the first address terminal of the table ROMRT5 for outputting the non-halftone index for the distributed halftone image, and the non-halftone image detection pixel number JS-C is output. D is the table ROMR
Input to the second address terminal of T5. Further, data of “55” indicating the total number of pixels in a 5 × 11 window for performing image area determination is input to the input terminal A of the subtractor SU1, and the number of all white and all black image detection pixels JS-E is calculated. Subtractor S
It is input to the input terminal B of U-1. The subtractor SU1 has A
−B is subtracted, and the data of the subtraction result is returned to the table RO.
Output to each second address terminal of MRT6 and RT7.

FIG. 48 is a graph of a non-halftone index for a distributed halftone image stored in the table ROMRT5, and FIG. 49 is a table ROMRT6, RT7.
7 is a graph of a non-halftone index for a intensive halftone image, stored in FIG. Here, the data x on the horizontal axis of each graph
1 and x2 are represented by the following “Formula 4” and “Formula 5”.

X1 = (number of non-halftone image detection pixels) / {(number of non-halftone image detection pixels) + (number of halftone image detection pixels)}

(A) In the case of the table ROMRT7 x2 = (the number of pixels of the centralized first dither image detection) / {(the total number of pixels)-(the number of all white and all black image detection pixels)} (b) the table ROMRT6 X2 = (centralized second dither image detection pixel number) / {(total pixel number)-(all white / all black image detection pixel number)}

The denominator of the data x2 is the subtractor SU1
Is calculated by As is clear from FIG. 48, the non-halftone index y1 indicating non-halftone likelihood for a distributed halftone image has the following value for the data x1. (A) When 0 ≦ x1 ≦ 0.5, y1 = 0, (b) When 0.5 <x1 ≦ 0.8, y1 = 2 × x1−
1, (c) When x1> 0.8, y1 = 1.

Further, as is apparent from FIG. 49, the non-halftone index y2 indicating non-halftone-likeness for the concentrated halftone image has the following value for the data x2. (A) When 0 ≦ x2 ≦ 2/3, y2 = 0, and (b) When x2> ２, y2 = 2 × x2-1.

In the graph of FIG. 49, the data x
If 2 is greater than 2/3, table ROMRT
7 and RT6, it is determined that the concentrated first dither image and the concentrated second dither image have been detected, respectively, and an H level concentrated first dither image determination signal and an H level second dither image determination signal are output. Is done. On the other hand, when the data x2 is 2/3 or less, it is determined that the centralized first dither image and the centralized second dither image are not detected from the table ROMs RT7 and RT6, respectively, and the L-level centralized first dither image is determined. A discrimination signal and an L-level concentrated second dither image discrimination signal are output. For convenience of explanation, the non-halftone index y1,
Although the value of y2 is a value from 0 to 1, in the circuit of FIG. 19, the non-halftone indices y1 and y2 are represented by 4-bit data.

The table ROMRT5 calculates the non-halftone index for the distributed halftone image from the stored table based on the data JS-C and JS-D input to the address terminals, and then compares the non-halftone index with the comparison selector. Output to the first input terminal of CS2. The table ROMRT7 stores the data JS-A input to the address terminal and the subtractor SU1.
, A non-halftone index for the intensive halftone image is obtained from the stored table based on the output data, and output to the first input terminal of the comparison / selector CS1, and the centralized first dither image discrimination is performed. The signal is output to restored data calculation section 112 in FIG. Further, a table ROMRT6
Calculates the non-halftone index for the intensive halftone image from the stored table based on the data JS-B input to the address terminal and the output data of the subtractor SU1, and then calculates the first halftone index of the comparison selector CS1. 2 as well as the centralized second dither image determination signal to the restored data calculation unit 112 in FIG.

After selecting the largest data among the input halftone indices, the comparison selector CS1 outputs the selected data to the first input terminal of the comparison selector CS2. Further, the comparison selector CS2 selects the largest data among the inputted halftone indices, and outputs the selected data to the data mixing unit 104 in FIG. 3 as image area determination data.

(6) Halftone Image Restoring Unit (6-1) Configuration and Operation of Each Unit The halftone image restoring unit 101 shown in FIG. Calculation unit 1
10, an edge enhancement amount calculation unit 111, and a restored data calculation unit 112. Hereinafter, features of the operations of the calculation units 109 to 113 will be described.

In the smoothing amount calculation unit 110, the spatial filter F11 of the 7 × 7 window W7 shown in FIG.
The spatial filter F12 of the 9 × 9 window W9 shown in FIG. 51 is switched and used according to the centralized second dither image determination signal as described later in detail. In both the spatial filter F11 of FIG. 50 and the spatial filter F12 of FIG.
7, the weighting coefficient for each pixel in W9 is 1. This is an image restored by the device of the present embodiment,
This is because a halftone image binarized by a pseudo halftone of the area gradation method, which expresses the image density using the number of black pixels per unit area, is targeted. In this embodiment, a square shape is used as a window for calculating the smoothing amount. However, the present invention is not limited to this, and as shown in FIG.
13 may be used.

Next, the necessity of calculating the edge enhancement amount in the edge enhancement amount calculation unit 111 will be described.

FIG. 55 shows an image in which a white image and a black image are inverted in the main scanning direction MS for every three pixels. The spatial frequency of this image in the main scanning direction MS is 1/6 [lp / pixel]. Now, the smoothing spatial filter F14 of the 1 × 6 window shown in FIG. 53 and the 1 × 6 window shown in FIG.
FIG. 5 shows the smoothed value calculation values CV1 and CV2 calculated respectively when scanning is performed on the scanning line MSL in the main scanning direction MS using the smoothing spatial filter F15 of the × 3 window.
It is shown in FIG. As is clear from the results shown in FIG. 55, when the image of FIG. 55 is smoothed using the spatial filter F15,
Although the spatial frequency component is preserved, on the other hand, when the image of FIG. 55 is smoothed using the spatial filter F14, it is understood that the spatial frequency component is lost. Therefore, the spatial filter for smoothing a window whose width in the scanning direction is a natural number of n pixels has a space higher than 1 / (2n) [lp / pixel] when scanning is performed in the scanning direction using the spatial filter. It can be seen that the frequency component is attenuated.
Accordingly, the spatial filters F11 and F12 shown in FIGS. 50 and 51 respectively have 1/14 [lp /
It can be seen that an image having a spatial frequency component higher than the pixel] and 1/18 [lp / pixel] cannot be sufficiently restored. Further, even if edge enhancement is performed on the image data after the smoothing process, it is impossible to recover the lost spatial frequency component. Therefore, in the present embodiment, the smoothing amount is obtained, and the edge enhancement amount corresponding to the edge component is calculated in order to recover the spatial frequency component lost by the smoothing process.

FIGS. 56 to 63 show spatial filters F21 to F28 for calculating the edge enhancement amount. Here, the spatial filter F21 is a spatial filter for calculating a difference in the number of black pixels between a pair of 3 × 7 windows W37a and W37b having a width of three pixels in the edge enhancement direction in the sub-scanning direction. And a spatial filter F22 for calculating a difference in the number of black pixels between a pair of 3 × 7 windows W37a and W37c having a width of three pixels in the edge enhancement direction in the sub-scanning direction. It is. Also,
The spatial filter F23 is a spatial filter for calculating the difference in the number of black pixels between a pair of 7 × 3 windows W73a and W73b having a width of three pixels in the edge enhancement direction in the main scanning direction. The spatial filter F24 is a spatial filter for calculating the difference in the number of black pixels between a pair of 7 × 3 windows W73a and W73c having a width of three pixels in the edge enhancement direction in the main scanning direction.

Further, the spatial filter F25 is a spatial filter for calculating a difference in the number of black pixels between a pair of windows WIa and WIb having a width of 22 pixels in the second oblique edge enhancement direction. And the spatial filter F2
Reference numeral 6 denotes a spatial filter for calculating a difference in the number of black pixels between a pair of windows WIa and WIc having a width of two pixels with respect to the second oblique edge enhancement direction. The spatial filter F27 is a spatial filter for calculating the difference in the number of black pixels between a pair of windows WJa and WJb having a width of two pixels in the first oblique edge enhancement direction, The spatial filter F28 is a spatial filter for calculating the difference in the number of black pixels between a pair of windows WJa and WJc having a width of two pixels in the first oblique edge enhancement direction.

Therefore, the spatial filters F21 to F2
8 is to calculate the edge enhancement amount in a total of eight directions of four directions in the main and sub scanning directions and four directions of the first and second diagonal directions with the target pixel (i = 5, j = 5) as the center. Can be.

FIGS. 64 to 71 respectively show spatial filters F31 to F38 corresponding to the spatial filters F21 to F28 and for obtaining an edge enhancement amount of an edge enhancement component having a higher spatial frequency.

The spatial filter F31 is a pair of 1 × 7 pixels having a width of one pixel in the edge enhancement direction in the sub-scanning direction.
Is a spatial filter for calculating the difference in the number of black pixels between the windows W17a and W17b.
Are a pair of 1 × 7 windows W17a and W17c having a width of one pixel in the edge enhancement direction in the sub-scanning direction.
This is a spatial filter for calculating the difference in the number of black pixels between the two. The spatial filter F33 is a spatial filter for calculating the difference in the number of black pixels between a pair of 7 × 1 windows W71a and W71b having a width of one pixel in the edge enhancement direction in the main scanning direction. Yes, spatial filter F34
Are a pair of 7 × 1 windows W71a and W71c having a width of one pixel in the edge enhancement direction in the main scanning direction.
This is a spatial filter for calculating the difference in the number of black pixels between the two. Further, the spatial filter F35 is a spatial filter for calculating the difference in the number of black pixels between a pair of windows WKa and WKb having a width of one pixel in the second oblique edge enhancement direction. The spatial filter F36 has a second
Is a spatial filter for calculating the difference in the number of black pixels between a pair of windows WKa and WKc having a width of one pixel with respect to the edge enhancement direction in the diagonal direction. Further, the spatial filter F37 includes a pair of windows WL each having a width of one pixel in the first oblique edge enhancement direction.
The spatial filter F38 is a spatial filter for calculating the difference in the number of black pixels between a and WLb. The spatial filter F38 includes a pair of windows WLa, This is a spatial filter for calculating the difference in the number of black pixels between WLc.

In these spatial filters F31 to F38, it is necessary to calculate the image density from a larger amount of pixel data in order to reduce the influence of the image pattern peculiar to the pseudo halftone. The width of the window is increased in a direction perpendicular to the direction. However, since it is necessary to prevent the emphasis on the image pattern peculiar to the pseudo halftone, the edge area determination unit 109
, And the above-described edge enhancement is performed only on the pixels determined to be within the edge area.

FIGS. 72 to 75 respectively show spatial filters F41 to F44 for calculating the edge area discrimination amount for discriminating the edge area.

The spatial filter F41 is composed of a pair of 4 × 7 pixels having a width of four pixels in the edge enhancement direction in the main scanning direction.
Is a spatial filter for calculating the difference in the number of black pixels between the windows W47a and W47b.
Are a pair of 4 × 7 windows W47a and W47c having a width of four pixels in the edge enhancement direction in the sub-scanning direction.
This is a spatial filter for calculating the difference in the number of black pixels between the two. The spatial filter F43 is a spatial filter for calculating a difference in the number of black pixels between a pair of windows WMa and WMb having a width of three pixels in the second oblique edge enhancement direction, The spatial filter F44 is a spatial filter for calculating the difference in the number of black pixels between a pair of windows WMc and WMd having a width of three pixels with respect to the first oblique edge enhancement direction.

Each window of the spatial filters F41 to F44 for calculating the edge area discrimination amount is a spatial filter for edge enhancement in order to eliminate the edge enhancement amount detected by the spatial filter for edge enhancement. Are set so that the width in the edge enhancement direction is larger than each of the windows. Each window of the spatial filter for smoothing is set to include each window of the spatial filter for edge enhancement and to include each window of the spatial filter for edge region discrimination.

(6-2) In-window counting section The in-window counting section 113 includes a first calculating section 113a, a second calculating section 113b, a third calculating section 113c, and a fourth calculating section 11c.
3d.

FIG. 20 is a block diagram of the first calculation unit 113a of the in-window counting unit 113 for counting the number of black pixels for each of seven continuous pixel data in the main scanning direction. Reference numeral 113a includes nine logic B circuits LB-60 to LB-68.

As shown in FIG. 20, seven consecutive pixel data D102 to D108 in the main scanning direction are input to the logic B circuit LB-60, and after the number of black pixels for each pixel data is counted. , The count value data DS10 is output, and seven pieces of pixel data D202 to D208 continuous in the main scanning direction are input to the logic B circuit LB-61, and the number of black pixels for each pixel data is counted. After that, the count value data DS11 is output. Hereinafter, similarly, the number of black pixels for each of seven pieces of pixel data D302 to D308, D402 to D408,..., D902 to D908 that are continuous in the main scanning direction for i = 3 to 9 is determined by the logical B circuit LB-62 to After being counted by the LB-68, data DS12 to DS18 of the count value are output.

FIG. 21 is a block diagram of the second calculation unit 113b of the in-window counting unit 113 for counting the number of black pixels for each of seven continuous pixel data in the sub-scanning direction. Reference numeral 113b includes nine logic B circuits LB-70 to LB-78.

As shown in FIG. 21, seven consecutive pixel data D201 to D801 in the sub-scanning direction are input to the logic B circuit LB-70, and after the number of black pixels for each pixel data is counted. , The count value data DS20 is output, and seven consecutive pixel data D202 to D802 in the sub-scanning direction are input to the logic B circuit LB-71, and the number of black pixels for each pixel data is counted. After that, the count value data DS21 is output. Hereinafter, similarly, the number of black pixels for each of seven pieces of pixel data D203 to D803, D204 to D804,. After being counted by the LB-78, data DS22 to DS28 of the count value are output.

FIG. 22 is a block diagram of the third calculation unit 113c of the in-window counting unit 113 for counting the number of black pixels for each of seven pieces of pixel data continuous in the first diagonal direction. The calculation unit 113c has nine logic B circuits LB-
80 to LB-88.

As shown in FIG. 22, seven pieces of pixel data D006, D105,.
600 is input to the logic B circuit LB-80, and after counting the number of black pixels for each pixel data, the count value data DS30 is output. Pixel data D007, D106,..., D6
01 is input to the logic B circuit LB-81, and after the number of black pixels for each pixel data is counted, the count value data DS31 is output. Hereinafter, similarly, each of the seven pieces of pixel data D107 and D20 which are continuous in the first oblique direction are similarly set.
, D701; D108, D207, ..., D70
D310, D409, ..., D904; D31
After the number of black pixels for 1, D410,..., D905 is counted by the logic B circuits LB-82 to LB-88, data DS32 to DS38 of count values are output.

FIG. 23 is a block diagram of the fourth calculation unit 113d of the in-window counting unit 113 for counting the number of black pixels for each of seven pieces of pixel data continuous in the second oblique direction. The calculation unit 113d has nine logic B circuits LB-
90 to LB-98.

As shown in FIG. 23, seven pieces of pixel data D004, D105,.
10 is input to the logic B circuit LB-90, and after counting the number of black pixels for each of the pixel data, data DS40 of the count value is output. Pixel data D003, D104,..., D60
9 is input to the logic B circuit LB-91, and after counting the number of black pixels for each pixel data, data DS41 of the count value is output. Hereinafter, similarly, each of the seven pieces of pixel data D103 and D20 that are continuous in the second oblique direction
, D709; D102, D203, ..., D70
D300, D401, ..., D906; D40
After the number of black pixels for 0, D501,..., D905, and D702 is counted by the logic B circuits LB-92 to LB-98, the count data DS42 to DS48 are output.

(6-3) Smoothing Amount Calculator FIG. 24 is a block diagram of the smoothing amount calculator 110 shown in FIG.

As shown in FIG. 24, data DS11, D
S12 is input to the adder AD71, and the data DS13,
DS14 is input to the adder AD72. The data DS15 and DS16 are input to the adder AD74, and the data DS17 is input to the adder AD75. Each data D
S11 to DS17 are six adders AD71 to AD7.
6 and the data of the addition result is added by the adder AD7.
6, 7 × which is the calculation result by the spatial filter F11
The data is output to the restored data calculation unit 112 and the adder AD81 as the 7-black pixel number second data.

The data DS10 and DS18 are input to the adder AD77, and the data DS20 and DS28 are input to the adder AD78. Pixel data D101, D80
1, D108, D808 and three "0" data are input to the logic B circuit LB-99, and the number of black pixels for each data is counted.
Output to D80. These data DS10, DS1
8, DS20, DS28, D101, D801, D10
8, D808 are added by the adders AD77 to AD80, and the data of the addition result is output from the adder AD80 to the adder AD81. The adder AD81 adds the input data, and outputs the resulting data to the restored data calculation unit 112 as 9 × 9 black pixel number data.

(6-4) Edge Enhancement Amount Calculation Unit The edge enhancement amount calculation unit 111 calculates the edge enhancement amount having the largest absolute value (hereinafter referred to as “edge”) among the edge enhancement amounts calculated using the spatial filters F21 to F28. A first calculation unit 111a for calculating the first enhancement data, and an edge enhancement amount having a maximum absolute value (hereinafter, edge enhancement) among the edge enhancement amounts calculated using the spatial filters F31 to F38. Second data for calculating the quantity second data).
And a calculation unit 111b.

FIG. 25 is a block diagram of the first calculator 111a of the edge enhancement amount calculator 111.

As shown in FIG. 25, data DS10 to DS12 are added by adders AD91 and AD92, and the resulting data is added from adder AD92 to subtractor SU.
11 input terminal A. Also, the data DS13
To DS15 are added by the adders AD93 and AD94, and the data of the addition result is output from the adder AD94 to the subtractor S
It is input to the input terminal B of U11 and the input terminal A of the subtractor SU12. Further, the data DS16 to DS18 are added by the adders AD95 and AD96, and the data of the addition result is input from the adder AD96 to the input terminal B of the subtractor SU12. The subtractor SU11 performs the subtraction of BA and outputs the data of the subtraction result to the comparison selector CS11 as the data of the calculation result of the spatial filter F21. Also,
The subtractor SU12 performs the subtraction of AB and outputs the data of the subtraction result to the comparison selector CS11 as the data of the calculation result of the spatial filter F22. In response to this, the comparison selector CS11 selects data having a larger absolute value from the input data and outputs the data to the comparison selector CS13.

Data DS20 to DS22 are added to adder AD.
97, AD98, and the data of the addition result is input from the adder AD98 to the input terminal A of the subtractor SU13. The data DS23 to DS25 are the adders A
D99 and AD100, and the data of the addition result is supplied from the adder AD100 to the input terminal B of the subtractor SU13.
And input to the input terminal A of the subtractor SU14. Further, the data DS26 to DS28 are added to the adders AD101,
The data is added by the AD 102, and the data of the addition result is input from the adder AD102 to the input terminal B of the subtractor SU14. The subtractor SU13 performs the subtraction of BA and outputs the data of the subtraction result to the comparison selector CS12 as the data of the calculation result of the spatial filter F23. Also, the subtractor SU
14 performs subtraction of AB and uses the data of the subtraction result as the data of the calculation result of the spatial filter F24 as the comparison selector C
Output to S12. In response, the comparison selector CS1
Reference numeral 2 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS13.

The data DS30 to DS32 are added to the adder AD.
103 and AD104, and the data of the addition result is supplied from the adder AD104 to the input terminal A of the subtractor SU15.
Is input to The data DS33 to DS35 are added by the adders AD105 and AD106, and the data of the addition result is input from the adder AD106 to the input terminal B of the subtractor SU15 and the input terminal A of the subtractor SU16. Further, the data DS36 to DS38 are added to the adder AD.
107 and AD108, and the data of the addition result is supplied from the adder AD108 to the input terminal B of the subtractor SU16.
Is input to The subtractor SU15 performs the subtraction of BA and outputs the data of the subtraction result to the comparison selector CS14 as the data of the calculation result of the spatial filter F25. Further, the subtracter SU16 performs the subtraction of AB and outputs the data of the subtraction result to the comparison selector CS14 as the data of the calculation result of the spatial filter F26. In response, the comparison selector CS14 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS16.

Data DS40 to DS42 are added to adder AD.
109 and AD110, and the data of the addition result is supplied from the adder AD110 to the input terminal A of the subtractor SU17.
Is input to The data DS43 to DS45 are added by the adders AD111 and AD112, and the data of the addition result is input from the adder AD112 to the input terminal B of the subtractor SU17 and the input terminal A of the subtractor SU18. Further, the data DS46 to DS48 are added to the adder AD.
113 and AD114, and the data of the addition result is supplied from the adder AD114 to the input terminal B of the subtractor SU18.
Is input to The subtractor SU17 performs the subtraction of BA and outputs the data of the subtraction result to the comparison selector CS15 as the data of the calculation result of the spatial filter F27. Further, the subtracter SU18 performs the subtraction of AB and outputs the data of the subtraction result to the comparison selector CS15 as the data of the calculation result of the spatial filter F28. In response to this, the comparison selector CS15 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS16.

The comparison selector CS13 selects data having a large absolute value from the input data and compares it with the comparison selector CS13.
17, and the comparison selector CS16 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS17. In response to this, the comparison selector CS17 selects data having a large absolute value from the input data, and outputs the selected data to the restored data calculation unit 112 as edge enhancement amount first data.

FIG. 26 is a block diagram of the second calculator 111b of the edge enhancement amount calculator 111.

As shown in FIG. 26, data DS13 is input to input terminal A of subtractor SU21, and data DS14
Is input to the input terminal B of the subtractor SU21 and the input terminal A of the subtractor SU22, and the data DS15 is supplied to the subtractor SU22.
Is input to the input terminal B. The subtractor SU21 performs the subtraction of BA, and outputs the data of the subtraction result to the spatial filter F31.
Is output to the comparison selector CS21 as the data of the calculation result of. Further, the subtractor SU22 performs the subtraction of AB and outputs the data of the subtraction result to the comparison selector CS21 as the data of the calculation result of the spatial filter F32. In response to this, the comparison selector CS21 selects data having a large absolute value from the input data and outputs the data to the comparison selector CS23.

The data DS23 is input to the input terminal A of the subtractor SU23, the data DS24 is input to the input terminal B of the subtractor SU23 and the input terminal A of the subtractor SU24,
Data DS25 is input to input terminal B of subtractor SU24. The subtractor SU23 performs the subtraction of BA and outputs the data of the subtraction result to the comparison selector CS22 as the data of the calculation result of the spatial filter F33. Also, the subtractor SU
24 performs subtraction of AB and uses the data of the subtraction result as the data of the calculation result of the spatial filter F34 as the comparison selector C
Output to S22. In response, the comparison selector CS2
Reference numeral 2 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS23.

Data DS33 is input to input terminal A of subtractor SU25, and data DS34 is input to input terminal B of subtractor SU25 and input terminal A of subtractor SU26.
Data DS35 is input to input terminal B of subtractor SU26. The subtractor SU25 performs the subtraction of BA, and outputs the data of the subtraction result to the comparison selector CS24 as the data of the calculation result of the spatial filter F35. Also, the subtractor SU
26 performs subtraction of AB and uses the data of the subtraction result as the data of the calculation result of the spatial filter F36 as the comparison selector C
Output to S24. In response, the comparison selector CS2
Reference numeral 4 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS26.

The data DS43 is input to the input terminal A of the subtractor SU27, the data DS44 is input to the input terminal B of the subtractor SU27 and the input terminal A of the subtractor SU28,
Data DS45 is input to input terminal B of subtractor SU28. The subtractor SU27 performs the subtraction of BA and outputs the data of the subtraction result to the comparison selector CS25 as the data of the calculation result of the spatial filter F37. Also, the subtractor SU
28 performs subtraction of AB and uses the data of the subtraction result as data of the calculation result of the spatial filter F38 as the comparison selector C
Output to S25. In response, the comparison selector CS2
Reference numeral 5 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS26.

The comparison selector CS23 selects the data having the larger absolute value from the input data and compares it with the comparison selector CS23.
27, and the comparison selector CS26 selects data having a large absolute value from the input data and outputs the selected data to the comparison selector CS27. In response, the comparison selector CS27 selects data having a large absolute value from the input data, and outputs the selected data to the restored data calculation unit 112 as edge enhancement second data.

The reason why the edge enhancement amount calculator 111 selects the absolute value edge enhancement amount from the edge enhancement amounts calculated by the respective spatial filters is as follows. That is, in the edge enhancement processing for multi-valued image data, edge components in all directions are added. On the other hand, in the edge enhancement processing for binary image data, an image pattern peculiar to a pseudo halftone is added. Output a small amount of edge enhancement in many cases. This is because the small edge enhancement amount has low reliability and the addition thereof may cause a large error.

(6-5) Edge Area Determining Unit FIG. 27 is a block diagram of the edge area determining unit 109 shown in FIG.

As shown in FIG. 27, data DS10 to DS13 are added by adders AD121 to AD123, and the added data is output from adder AD123 to input terminal A of subtractor SU31. Data D
S14 to DS17 are adders AD124 to AD126
And the data of the addition result is added by the adder AD12.
6 to the input terminal B of the subtractor SU31. The subtractor SU31 performs the subtraction of BA, and outputs the data of the subtraction result to the comparison selector CS31 as the data of the calculation result of the spatial filter F41.

The data DS21 to DS24 are added to the adder AD.
127 to AD129, and data of the addition result is output from the adder AD129 to the input terminal A of the subtractor SU32. The data DS25 to DS28 are added by the adders AD130 to AD132, and the data of the addition result is sent from the adder AD132 to the subtractor SU32.
Is output to the input terminal B. The subtractor SU32 performs the subtraction of AB, and outputs the data of the subtraction result to the spatial filter F42.
Is output to the comparison selector CS31 as data of the calculation result of (1).

The data DS31 to DS34 are added to the adder AD.
The data is added by 133 to AD135, and the data of the addition result is output from the adder AD135 to the input terminal A of the subtractor SU33. The data DS35 to DS38 are added by the adders AD136 to AD138, and the data of the addition result is sent from the adder AD138 to the subtractor SU33.
Is output to the input terminal B. The subtractor SU33 performs the subtraction of AB, and outputs the data of the subtraction result to the spatial filter F43.
Is output to the comparison / selector CS32 as data of the calculation result.

The data DS40 to DS43 are added to the adder AD.
139 to AD141, and data of the addition result is output from the adder AD141 to the input terminal A of the subtractor SU34. The data DS44 to DS47 are added by the adders AD142 to AD144, and the data of the addition result is sent from the adder AD144 to the subtractor SU34.
Is output to the input terminal B. The subtractor SU34 performs the subtraction of BA, and outputs the data of the subtraction result to the spatial filter F44.
Is output to the comparison / selector CS32 as data of the calculation result.

The comparison selector CS31 selects data having a large absolute value from the input data and selects the data having the larger absolute value.
33, and the comparison selector CS32 selects data having a large absolute value from the input data, and
Output to S33. In response, the comparison selector CS3
Numeral 3 selects data having a large absolute value from the input data and outputs the selected data to the absolute value circuit AB1. The absolute value circuit AB1 uses the data of the absolute value of the input data as the edge area discrimination amount,
12 is output.

The reason why the edge area discriminating section 109 outputs the absolute value data as the edge area discriminating amount is that the information on the direction of the edge component corresponding to the positive or negative edge component amount is calculated by the restored data calculation. This is because it is unnecessary in the unit 112.

(6-6) Restored Data Calculator FIG. 28 is a block diagram of the restored data calculator 112 shown in FIG.

As shown in FIG. 28, data of the number of 9 × 9 black pixels is input to the multiplier 201, and the 9 × 9 window W
9 is multiplied by 49/81 to convert the total number of pixels in 9 into the total number of pixels in the 7 × 7 window W7, and the multiplied data is output to the input terminal A of the selector 202. Is done. The 7 × 7 black pixel number second data is input to the input terminals A of the comparators 203 and 204 and also to the input terminal B of the selector 202.
The data of the predetermined threshold TJ1 is input to the input terminal B of the comparator 203, and the comparator 203 outputs an H level signal to the clear terminal of the multiplier 206 via the NOR gate 205 when A <B, On the other hand, otherwise, an L-level signal is output similarly. Further, the data of “49-TJ1” is input to the input terminal B of the comparator 204,
Outputs an H-level signal to the clear terminal of the multiplier 206 via the NOR gate 205 when A> B, and outputs an L-level signal otherwise at other times.

The centralized first dither image discrimination signal is supplied to the comparison selector 20 via the OR gate 209 and the OR gate 210.
7 is input to the clear terminal of the comparison selector 207 via the OR gate 209 and the OR gate 210, and is also input to the selection terminal SEL of the selector 202. .

The selector 202 is an H-level centralized second
When the dither image discrimination signal is input to the selection terminal SEL, the data input to the input terminal A is selected,
When the L-level centralized second dither image discrimination signal is input to the selection terminal SEL, the data input to the input terminal B is selected and output to the multiplier 211. I do. The multiplier 211 is provided to convert from 50 gradations to 64 gradations. After multiplying input data by a multiplier of 63/49, the multiplication result data is used as an input terminal A of the addition circuit 213 as a smoothing amount. Output to

The first data of the amount of edge enhancement is output from the comparison selector 20.
7, and the second data of the amount of edge enhancement is input to the multiplier 206. The multiplier 206 converts the size of each window of each spatial filter used when calculating the first data of edge enhancement amount and each window of each spatial filter used when calculating the second data of edge enhancement amount. And multiplies the input data by 3, and outputs the multiplication result data to the input terminal A of the comparison selector 207. Here, the H-level signal is output from the multiplier 2
When the data is input to the clear terminal 06, its output data is cleared to "0".

The data of the edge area discrimination amount is supplied to the comparator 208.
Is input to an input terminal A of the comparator 208, data of a predetermined threshold value TJ2 is input to an input terminal B of the comparator 208,
8 outputs an H level signal to the comparison selector 207 via the OR gate 210 when A <B, and outputs an L level signal otherwise. Comparison selector 207
When an L-level signal is input to the clear terminal, the data having the largest absolute value is selected from the data input to each of the input terminals A and B and output to the multiplier 212. When an H level signal is input to the terminal, the output data is cleared and the data of "0" is
12 is output. The multiplier 212 adds (6) to the input data in order to mix an appropriate amount of edge enhancement with the smoothing amount.
3 × 0.4 / 21), and outputs the multiplication result data to the input terminal B of the addition circuit 213 as an edge enhancement amount. Further, the addition circuit 213 converts the data of the smoothing amount and the data of the edge enhancement amount input to the input terminals A and B into
After the addition as described in detail later, data of the addition result is output to the data mixing unit 104 as halftone restored image data.

In the restored data calculation unit 112 configured as described above, the selector 202 selects data corresponding to the number of 9 × 9 black pixels or selects 7 × 9 pixels in accordance with the centralized second data image discrimination signal. Both the selection and the selection of the 7th black pixel number second data are performed. The concentration type first dither image has a one-cycle density change every four dots in the main / sub scanning direction. Therefore, a window larger than a 4 × 4 window is required to calculate the smoothing amount. In this embodiment, a 7 × 7 window W7 is used. On the other hand, in the concentrated second dither image, there is a one-cycle density change every four dots in the oblique direction. for that reason,
In order to calculate the smoothing amount, a window larger than a window of (square root of 2 × 4) × (square root of 2 × 4) is necessary. In the present embodiment, a margin is added and 9 × 9
Window W9 is used. Note that a 7 × 7 window W7 is used for calculating the smoothing amount for the dispersed halftone image.

In order to prevent the occurrence of moire, a window having a size which is an integral multiple of the dither period is desirable.
Therefore, it is more preferable to use a 4 × 4 window or an 8 × 8 window for the centralized first dither image in order to calculate the smoothing amount. For the centralized second dither image, an 8 × 8 window whose side is parallel to the main / sub scanning direction is inclined by 45 degrees with respect to the main / sub scanning direction in order to calculate the smoothing amount. It is more preferable to use a specified window.

The restored data calculation unit 112 uses the concentrated first dither image determination signal and the concentrated second dither image determination signal in order to determine whether or not the pixel of interest is in the edge area. When the edge area discrimination amount is smaller than a predetermined threshold value TJ2 (when the output signal of the comparator 208 is at the H level), or one of the concentrated first dither image discrimination signal and the concentrated second dither image discrimination signal Is high (the output signal of the OR gate 209 is high).
Level), the output signal of OR gate 210 is H
At this time, the output data of the comparison selector 207 is cleared to “0”, the edge enhancement amount becomes “0”, and no edge enhancement is performed. This is because the spatial frequency of the dither pattern is relatively low in the area of the centralized first or second dither image, and the edge area determining unit 109 for detecting the distributed halftone image is an edge area. This is because there is a possibility that an edge discrimination amount that may be erroneously determined to be output. In the present embodiment, preferably, the threshold value TJ2 is 4.

Further, in order to convert the difference in size between the window of the spatial filter used to calculate the first data of edge enhancement and the window of the spatial filter used to calculate the second data of edge enhancement. , A multiplier 206 is provided. The conversion ratio needs to be set so that the edge enhancement amount calculated using the window with respect to the image having the spatial frequency of one window is selected.

The number of small pixels is equal to a predetermined threshold value TJ1.
At this time (when both output signals of the comparators 203 and 204 are at the L level), the conversion data of the edge enhancement amount second data by the multiplier 206 is cleared to “0”. This is the edge enhancement amount second data, which is an edge enhancement amount calculated for a thin line-shaped image having a width of 1. In such an image, as described above, the number of minority pixels is relatively small. Because. Further, by the processing, when the number of the small number of pixels increases, it is possible to prevent the emphasis on the pseudo halftone image pattern. In the present embodiment, the threshold value TJ1 is preferably 8.

FIG. 29 shows the operation of the adder 21 shown in FIG.
3 is a block diagram of FIG.

The adder circuit 213 has a 7
A circuit for adding the edge enhancement amount of a bit and the smoothing amount of 6 bits, comprising an adder AD151 and comparators CM51, CM
M52, a bit truncation circuit BAC that truncates and outputs one sign bit and one most significant bit, and a selector SE
1 and a multiplier MU51 for halving the input data and outputting the data.

The data of the edge enhancement amount output from the multiplier 212 and the data of the smoothing amount output from the multiplier 211 are input to the adder AD151, and the adder AD151
The 8-bit data including the 1-bit code output from 1 is output to each input terminal A of the comparators CM51 and CM52, and is also output to the selector SE1 via the bit truncation circuit BAC. Data of "0" is input to the input terminal B of the comparator CM51, the comparator CM51 compares the data input to the input terminals A and B, and the data input to the input terminal A is input to the input terminal B. When the data is smaller than the input data, an H level comparison result signal is output to the selection signal input terminal SELA of the selector SE1, and otherwise, an L level comparison result signal is similarly output. Also, data of “64” is input to the input terminal B of the comparator CM52, the comparator CM52 compares the data input to the input terminals A and B, and the data input to the input terminal A is When the data is equal to or more than the data input to the selector SE1, the comparison result signal at the H level is sent to the selection signal input terminal S
The signal is output to the ELB, and at other times, the L-level comparison result signal is output in the same manner. The selector SE1 outputs “0” data input to the input terminal A when the signal input to the selection signal input terminal SEA is at an H level, and outputs a signal input to the selection signal input terminal SEB at an H level. In this case, the data of "64" inputted to the input terminal A is outputted, and when the signals inputted to the respective selection signal input terminals SEA and SEB are both at L level, the 6-bit data inputted to the input terminal C is outputted. Output. The data output from the selector SE1 is output to the data mixing unit 104 as restored halftone multivalued image data via the multiplier MU51.

Adder circuit 213 configured as described above
Is obtained by adding the edge enhancement amount and the smoothing amount, rounding less than 0 and 64 or more out of the addition result data to 0 and 63, and then truncating the lower 1 bit to have a certain value from 0 to 31. Can operate on bit data,
The data of this calculation result is output to the data mixing unit 104 as restored halftone multivalued image data.

(7) Other Embodiments In the above embodiment, the centralized halftone image is detected based on the change of the peripheral distribution shown in FIG. 41. However, the five halftone images shown in FIG. Windows W4a to W4
A pattern matching method using d may be used.

In FIG. 76, 4 × 4 pixels including the pixel of interest *
Window W4a and four windows 44 adjacent to the window W4a in the first and second oblique directions.
By performing image pattern matching between the × 4 windows W4b to W4d and counting the number of pixels in which each pixel data does not match, a halftone discrimination value for a concentrated halftone image can be obtained. The four windows W4b to W4d are used to simultaneously support two types of dot-concentrated systematic dither images having screen angles of 0 and 45 degrees.

Now, for example, the image pattern PA shown in FIG.
Table 1 shows the results of counting the number of non-matching pixels for T1, the image pattern PAT2 in FIG. 78, and the image pattern PAT3 in FIG. 79 using the above-described pattern matching method.

[0162]

[Table 1]

As is clear from Table 1, in the case of the image pattern PAT1 shown in FIG. 77 which is a non-halftone image pattern, the number of non-matching pixels has a large value. The validity of the method was proved.

[0164]

As described in detail above, according to the image processing apparatus of the present invention, the binary image data binarized by the pseudo halftone and the binary image data by the non-halftone using the predetermined threshold value. In the input binary image data including the binarized binary image data, two pixels corresponding to the pixel of interest and a plurality of pixels around the pixel of interest.
Based on the value image data, for each pixel of the input binary image data, a first type pseudo halftone image-likeness and a second type pseudo halftone different from the first type pseudo halftone Determining means for determining the likelihood of a halftone image and calculating a determination value indicating the likelihood of a pseudo-halftone image and the likelihood of a non-halftone image using the results of these determinations; Is restored to multi-valued image data by a restoration process in a case where the binary image data is pseudo halftone image data for each pixel based on the binary image data corresponding to the pixel of interest and a plurality of pixels surrounding the pixel of interest. And first restoration means for restoring the binary image data to multi-valued image data by a restoration process when the binary image data is the non-halftone image data for each pixel based on the input binary image data. Second restoration means Mixing the multi-valued image data restored by the first restoration means and the multi-valued image data restored by the second restoration means at a mixing ratio corresponding to the discrimination value calculated by the discrimination means. Mixing means for outputting the mixed multi-valued image data.

Therefore, it is possible to perform restoration processing on multi-valued image data in accordance with the discrimination values corresponding to various image readers and various pseudo halftone binarization circuits. Restoring non-halftone binarized image data and various pseudo halftone binarized image data into multi-valued image data corresponding to the original image more faithfully as compared with the conventional example. There is an advantage that can be.

[Brief description of the drawings]

FIG. 1 is a longitudinal sectional view of a mechanism of a facsimile apparatus according to an embodiment of the present invention.

FIG. 2 is a block diagram of a signal processing unit of the facsimile apparatus shown in FIG.

FIG. 3 is a block diagram of an image restoration processing unit illustrated in FIG. 2;

FIG. 4 is a block diagram of a 10 × 21 matrix memory circuit shown in FIG. 3;

FIG. 5 is a block diagram of a first calculation unit of the dither determination unit shown in FIG. 3;

FIG. 6 is a block diagram of a second calculation unit of the dither determination unit shown in FIG.

FIG. 7, FIG. 6, FIG. 18, and FIG. 20 to FIG.
3 is a block diagram of a logic B circuit illustrated in FIG.

FIG. 8 is a block diagram of a third calculation unit of the dither determination unit shown in FIG.

FIG. 9 is a block diagram of a fourth calculation unit of the dither determination unit shown in FIG. 3;

FIG. 10 is a block diagram of a fifth calculation unit of the dither determination unit shown in FIG.

11 is a block diagram of a sixth calculating unit of the dither determining unit shown in FIG.

12 is a block diagram of a seventh calculator of the dither determination unit shown in FIG.

13 is a block diagram of a first calculation unit of the adjacent state determination unit shown in FIG.

FIG. 14 is a block diagram of a main / sub scanning direction adjacent number counter shown in FIG. 13;

FIG. 15 is a block diagram of a second calculator of the adjacent state determiner shown in FIG. 3;

FIG. 16 is a block diagram of a third calculator of the adjacent state determiner shown in FIG. 3;

FIG. 17 is a block diagram of the 5 × 11 matrix memory circuit shown in FIG. 3;

FIG. 18 is a block diagram of a determination data counting unit illustrated in FIG. 3;

FIG. 19 is a block diagram of a discrimination data signal generator shown in FIG. 3;

20 is a block diagram of a first calculation unit of the intra-window counting unit shown in FIG.

21 is a block diagram of a second calculation unit of the in-window counting unit shown in FIG.

FIG. 22 is a block diagram of a third calculating unit of the intra-window counting unit shown in FIG. 3;

FIG. 23 is a block diagram of a fourth calculating unit of the in-window counting unit shown in FIG. 3;

FIG. 24 is a block diagram of a smoothing amount calculator shown in FIG. 3;

FIG. 25 is a diagram illustrating a first example of the edge enhancement amount calculation unit illustrated in FIG. 3;
It is a block diagram of a calculation part.

26 is a diagram illustrating a second example of the edge enhancement amount calculation unit illustrated in FIG. 3;
It is a block diagram of a calculation part.

FIG. 27 is a block diagram of an edge area determining unit shown in FIG. 3;

FIG. 28 is a block diagram of a restored data calculation unit shown in FIG. 3;

FIG. 29 is a block diagram of the adder circuit shown in FIG. 28;

FIG. 30 is a diagram showing each pixel data in a 10 × 21 window used in the present embodiment.

31 is a reference range of each pixel at the time of determination by the adjacent state determination unit and the dither determination unit shown in FIG. 3, a reference range of each pixel at the time of determination by the determination data signal generation unit shown in FIG. 3, and FIG. FIG. 7 is a diagram illustrating a reference range of each pixel at the time of restoration in the illustrated halftone image restoration unit.

FIG. 32 is a diagram illustrating an example of a non-halftone image obtained when a character image is read and binarized using a predetermined threshold value.

FIG. 33 is a diagram showing an example of a pseudo halftone binary image obtained when a uniform density chart is read and then binarized by an error diffusion method.

FIG. 34 is a diagram illustrating an example of a pseudo halftone binary image obtained when a photographic image is read and then binarized by a dot concentration type systematic dither method with a screen angle of 0 degrees.

FIG. 35 is a diagram illustrating an example of a pseudo halftone binary image obtained when a photographic image is read and then binarized by a dot concentration type organized dither method with a screen angle of 45 degrees.

FIG. 36 is a graph showing the number of black pixels in a 7 × 7 window and the number of adjacent pixels in the four main scanning directions with respect to the number of black pixels.

37 is a diagram showing black pixel number data S10 to S17 of each window used in the dither determination unit shown in FIG.

38 is a diagram showing black pixel number data S20 to S27 of each window used in the dither determination unit shown in FIG.

FIG. 39 is a diagram illustrating black pixel number data S30 to S37 of each window used in the dither determination unit illustrated in FIG. 3;

FIG. 40 is a diagram illustrating black pixel number data S40 to S47 of each window used in the dither determination unit illustrated in FIG. 3;

FIG. 41 is a graph showing a peripheral distribution of the number of black pixels with respect to each data of the number of black pixels in each window continuous in the main scanning direction or the sub-scanning direction.

FIG. 42 is a diagram showing adjacent pixels in a 7 × 7 window in the main scanning direction.

FIG. 43 is a diagram showing adjacent pixels in a 7 × 7 window in the sub-scanning direction.

FIG. 44 is a diagram illustrating an example of a case where a small number of pixels are not present around a target pixel, but a relatively large number of small pixels are present in a window for adjacent determination.

FIG. 45 is a diagram illustrating an example of a case where a linear non-halftone image approaches a 7 × 7 window.

FIG. 46 is a diagram illustrating another example in which a linear non-halftone image approaches a 7 × 7 window.

FIG. 47 is a diagram showing a detection signal of each pixel in the 5 × 11 matrix memory circuit shown in FIG. 3;

FIG. 48 is a graph of a non-halftone index for a distributed halftone image stored in the table ROM of the discrimination data signal generation unit shown in FIG. 3;

FIG. 49 is a graph of a non-halftone index for a concentrated halftone image stored in the table ROM of the discrimination data signal generator shown in FIG. 3;

50 is a diagram showing a spatial filter used in the smoothing value calculator shown in FIG. 3 for counting the number of black pixels in a 7 × 7 window.

FIG. 51 is a diagram showing a spatial filter used in the smoothing value calculator shown in FIG. 3 for counting the number of black pixels in a 9 × 9 window.

FIG. 52 is a diagram showing a modification of the spatial filter used to calculate the smoothed value.

FIG. 53 is a diagram showing an example of a smoothing spatial filter used to explain the necessity of calculating an edge enhancement amount.

FIG. 54 is a diagram showing another example of the smoothing spatial filter used to explain the necessity of calculating the amount of edge enhancement.

FIG. 55 shows an example of an image used to explain the necessity of calculating the amount of edge enhancement, and respective calculated values when the smoothing spatial filters of FIGS. 53 and 54 are applied to the image in the main scanning direction. FIG.

FIG. 56 is a diagram showing a spatial filter F21 for calculating an edge enhancement amount used in the edge enhancement amount calculation unit shown in FIG. 3;

FIG. 57 is a diagram illustrating a spatial filter F22 used in the edge enhancement amount calculation unit illustrated in FIG. 3 for calculating the edge enhancement amount.

58 is a diagram illustrating a spatial filter F23 used in the edge enhancement amount calculation unit illustrated in FIG. 3 for calculating the edge enhancement amount.

59 is a diagram showing a spatial filter F24 for calculating an edge enhancement amount used in the edge enhancement amount calculation unit shown in FIG.

FIG. 60 is a diagram illustrating a spatial filter F25 for calculating an edge enhancement amount, which is used in the edge enhancement amount calculation unit illustrated in FIG. 3;

FIG. 61 is a diagram illustrating a spatial filter F26 for calculating an edge enhancement amount, which is used in the edge enhancement amount calculation unit illustrated in FIG. 3;

62 is a diagram showing a spatial filter F27 for calculating an edge enhancement amount used in the edge enhancement amount calculation unit shown in FIG.

FIG. 63 is a diagram showing a spatial filter F28 for calculating an edge enhancement amount used in the edge enhancement amount calculation unit shown in FIG. 3;

FIG. 64 is a diagram illustrating a spatial filter F31 for calculating an edge enhancement amount, which is used in the edge enhancement amount calculation unit illustrated in FIG. 3;

FIG. 65 is a diagram illustrating a spatial filter F32 for calculating an edge enhancement amount, which is used in the edge enhancement amount calculation unit illustrated in FIG. 3;

FIG. 66 is a diagram illustrating a spatial filter F33 for calculating an edge enhancement amount used in the edge enhancement amount calculation unit illustrated in FIG. 3;

FIG. 67 is a diagram illustrating a spatial filter F34 for calculating an edge enhancement amount, which is used in the edge enhancement amount calculation unit illustrated in FIG. 3;

FIG. 68 is a diagram illustrating a spatial filter F35 for calculating an edge enhancement amount used in the edge enhancement amount calculation unit illustrated in FIG. 3;

69 is a diagram illustrating a spatial filter F36 used in the edge enhancement amount calculation unit illustrated in FIG. 3 for calculating the edge enhancement amount.

70 is a diagram illustrating a spatial filter F37 used in the edge enhancement amount calculation unit illustrated in FIG. 3 for calculating an edge enhancement amount.

71 is a diagram showing a spatial filter F38 for calculating an edge enhancement amount used in the edge enhancement amount calculation unit shown in FIG.

FIG. 72 is a diagram showing a spatial filter F41 for calculating an edge area discrimination amount used in the edge area discrimination unit shown in FIG. 3;

73 is a diagram illustrating a spatial filter F42 used in the edge area determination unit illustrated in FIG. 3 for calculating an edge area determination amount.

74 is a diagram illustrating a spatial filter F43 used in the edge area determination unit illustrated in FIG. 3 for calculating an edge area determination amount.

75 is a diagram illustrating a spatial filter F44 used in the edge area determination unit illustrated in FIG. 3 for calculating an edge area determination amount.

FIG. 76 shows five pattern matching windows W4 for explaining a pattern matching method which is a modification for calculating a halftone index for a concentrated halftone image.
It is a figure showing a through W4e.

77 shows a first image pattern PAT1 used to show a calculation example by the pattern matching method performed using the pattern matching window shown in FIG. 76.
FIG.

78. A second image pattern PAT2 used to show a calculation example by the pattern matching method performed using the pattern matching window shown in FIG.
FIG.

79 is a third image pattern PAT3 used to show an example of calculation by the pattern matching method performed using the pattern matching window shown in FIG. 76.
FIG.

[Explanation of symbols]

 Reference numeral 62: image restoration processing unit, 100: 10 × 21 matrix memory circuit, 101: halftone image restoration unit, 102: image area discrimination unit, 103: simple multi-value conversion unit, 104: data mixing unit, 105: adjacent state judgment Unit 106: dither determination unit 107: 5 × 11 matrix memory circuit 108: determination data generation unit 109: edge area determination unit 110: smoothing amount calculation unit 111: edge enhancement amount calculation unit 112: restored data Calculator, 113: Window calculator, 114: Discrimination data signal generator, RT5, RT6, RT7: Table ROM, SU1: Subtractor, CS1, CS2: Comparison selector.

Continuation of front page (58) Fields investigated (Int.Cl. ⁷ , DB name) H04N 1/40-1/409 B41J 2/52 G06T 5/00

Claims (4)

(57) [Claims] 1. An input binary image including binary image data binarized in a pseudo halftone and binary image data binarized in a non-halftone using a predetermined threshold value. Based on the binary image data corresponding to the pixel of interest and a plurality of pixels surrounding the pixel of interest, each pixel of the input binary image data is used to represent a pseudo-halftone image of the first type. And a second kind of pseudo-halftone image likelihood different from the first kind of pseudo-halftone image is discriminated, and the discrimination indicating the pseudo-halftone image likeness and the non-halftone image-likeness using these discrimination results. Determining means for calculating a value; and, for each pixel, the binary image data is simulated based on the binary image data corresponding to the pixel of interest and a plurality of pixels surrounding the pixel of interest in the input binary image data. By the restoration process when the image data is halftone A first restoring means for restoring the value image data, for each pixel based on the binary image data the input,
Second restoration means for restoring to multi-valued image data by restoration processing when the binary image data is the non-halftone image data; multi-valued image data restored by the first restoration means; Mixing means for mixing the multi-valued image data restored by the restoration means of No. 2 at a mixing ratio according to the discrimination value calculated by the discrimination means, and outputting the mixed multi-valued image data. An image processing apparatus characterized by the above-mentioned. 2. The method according to claim 1, wherein the discriminating means uses a result of discriminating the likeness of a first type of pseudo-halftone image and a result of discriminating a second type of pseudo-halftone image for a plurality of pixels including the pixel of interest. 2. The image processing apparatus according to claim 1, wherein a discriminant value indicating the likelihood of a pseudo halftone image and the likelihood of a non-halftone image for the pixel of interest is calculated. 3. The image processing apparatus according to claim 1, wherein the image likeness of the first type of pseudo halftone image is a dot concentration type pseudo halftone image. 4. An input binary image including binary image data binarized in a pseudo halftone and binary image data binarized in a non-halftone using a predetermined threshold value. Based on the binary image data corresponding to the pixel of interest and a plurality of pixels around the pixel of interest, each pixel of the input binary image data is used to determine the pseudo-halftone image-likeness and the non-halftone image. Determining means for calculating a determination value indicating image likeness; and, for each pixel, based on binary image data corresponding to the pixel of interest and a plurality of pixels around the pixel of interest in the input binary image data, First restoration means for restoring to multi-valued image data by restoration processing when the value image data is pseudo halftone image data; and for each pixel based on the input binary image data,
Second restoration means for restoring to multi-valued image data by restoration processing when the binary image data is the non-halftone image data; multi-valued image data restored by the first restoration means; Mixing means for mixing the multi-valued image data restored by the restoration means of No. 2 at a mixing ratio according to the discrimination value calculated by the discrimination means, and outputting the mixed multi-valued image data; The discriminating means includes: first discriminating means for calculating a discriminating value indicating the likeness of a dot-dispersed pseudo-halftone image for each pixel of the input binary image data; A second discriminating means for calculating a discriminating value indicating the likelihood of a dot-concentrated pseudo halftone image for each pixel, and a discriminating value calculated by the first discriminating means.
Comparing the discrimination value calculated by the discrimination means with a larger discrimination value and outputting the selected discrimination value as a discrimination value indicating the likelihood of the pseudo halftone image and the non-halftone image. An image processing apparatus comprising: a selection unit.