JPH099061A - Picture boundary area detecting device and picture boundary area correcting device - Google Patents

Abstract

PURPOSE: To correctly detect a picture boundary area based on pseudo gradation picture data by discriminating whether or not a picture element to be detected constitutes the boundary area based on detected picture difference. CONSTITUTION: When pseudo gradation picture data is inputted, a density difference detecting part 2 outputs density difference data concerning the picture element corresponding to respective kinds of pseudo gradation picture data to a halftone picture data converting part 4. Then, a converting part 4 permits a halftone converting RAM6 to store pseudo gradation picture data and also makes pseudo gradation picture data which is stored in a picture element area with the object picture element as a center into a block. Then, the filter coefficient of a spatial filter is read from ROM5 based on density difference data inputted from the detection block 2 so as to execute spatial filtering. Then, a picture element density converting part 7 converts picture element density by executing linear interpolation by a projecting method. After that, a pseudo gradation picture data converting part 8 converts halftone picture data where picture element density is converted into new pseudo gradation picture data.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image transmission device such as a facsimile device, an image recording device such as a copying machine or a printer device, an image recognition device for performing pattern recognition or image analysis, and an electronic book or image filing. The present invention relates to an image boundary area detection device and an image boundary area correction device used for image processing of image data in an image storage device such as a device.

[0002]

2. Description of the Related Art For example, in a facsimile machine, G
Four machines read and print at a pixel density of 200 dpi or 400 dpi, and the G3 machine has 8 x 7.7 pixels / pixel.
Reading and printing are performed at mm ² or 8 × 3.85 pixels / mm ² . Therefore, when the G4 machine transmits image data to the G3 machine, the pixel density is converted so as to match the printing characteristics of the G3 machine. In this case, when the pixel density is enlarged and converted, in a facsimile apparatus, in order to prevent blurring of an image, a sharpening process for sharpening a boundary region of an image, which is a portion where a density difference in an original image largely changes, by a spatial filter. Is being done. Therefore, prior to the sharpening process, it is necessary to determine whether or not the detection target pixel corresponding to the image data of the sharpening process constitutes a boundary region. In such a case, conventionally, the detection target pixel is The determination is made by detecting the density difference between the pixel area including the and the other pixel area. A pixel density conversion device 51 shown in FIG. 5 is known as an application of such a density difference detection device.

A pixel density conversion device 51 shown in FIG. 1 has a pseudo-gradation in which the density of an image is pseudo-graded by a binary value by increasing / decreasing the number of black pixels included in a pixel region of 5 × 5 matrix, for example. The pixel density of the gradation image data is converted, and includes a halftone image data conversion unit 52, a boundary area determination unit 53, a pixel density conversion unit 54, and a pseudo gradation image data conversion unit 55.

In the pixel density conversion device 51, the halftone image data conversion unit 52 converts the input pseudo gradation image data into halftone image data. In this case, in order to determine whether or not to perform the sharpening process, the boundary area determination unit 53
However, as shown in FIG. 7, the pseudo gradation image data is divided into blocks for each 3 × 3 pixel region, and the blocked pixel region 6 is formed.
It is determined whether or not the boundary area is included in 2. Specifically, the boundary area determination unit 53 is included in each of the pixel area 62 including the detection target pixels 61, 61 ... Of the sharpening processing target and the other pixel areas 63, 63. The number of black pixels 64, 64, ... Is found, and the number of black pixels is used as the density of each of the pixel regions 62, 63, 63. Next, the boundary area determination unit 53 obtains a density difference between the density of the pixel area 62 and the average value of the density of the pixel areas 63, 63, ... If the density difference is “4” or more, the pixel area is determined. It is determined that a boundary area is included in 62, and the determination result is used as determination data to obtain the halftone image data conversion unit 52.
Output to When it is determined that the boundary area is not included, the halftone image data conversion unit 52 performs the spatial filter processing by the average value filter in which the values of all the elements of the 5 × 5 matrix are “1”. , Pixel area 6
All the detection target pixels 61, 61 ... In 2 are averaged. That is, in this process, in order to reduce the moire of the image, the halftone image data conversion unit 52, for each detection target pixel 61, the pixel area of a 5 × 5 matrix centered on the detection target pixel 61. The average value of the densities is obtained, and the average value is set as the new density of the detection target pixel 61.

On the other hand, when it is determined that the boundary area is included, the halftone image data conversion unit 52 uses the image sharpening filter instead of the average value filter to detect all the pixels in the pixel area 62. By converting the target pixels 61, 61, ... into sharpened halftone image data, it is possible to prevent blurring of the image that occurs when the pixel density is increased.
By performing these processes on all the pseudo gradation image data, the conversion from the pseudo gradation image data to the multivalued halftone image data is completed. Next, the pixel density conversion unit 54 converts the pixel density by a projection method or the like, and then the pseudo gradation image data conversion unit 55 converts the halftone image data into new pseudo gradation image data. Then, the converted pseudo gradation image data is output to the communication line by the transmission device (neither is shown).

[0006]

However, the conventional pixel density conversion device 51 has the following problems. That is, the pixel density conversion device 51 determines whether or not a boundary area is included in the blocked pixel area 62, and when it is determined that the boundary area is included, the pixel area 62 is determined.
All of the detection target pixels 61, 61 ... Are sharpened uniformly. FIG. 6 shows a pixel 5 which is a target of the sharpening process.
, 56, ..., As can be seen from this figure, the pixels that do not form the boundary area are also subject to the sharpening process. Therefore, as a result of many pixels being sharpened excessively, the image disturbance due to the noise generated by the sharpening is generated in the boundary region of the image based on the new pseudo gradation image data after the pixel density conversion. There is a problem that an image is generated and becomes very difficult to see.

Further, in the image recognition device or the like, since it is determined whether or not the boundary region is included in the pixel region as in the pixel density conversion device 51, the pixel which is determined to form the boundary region is determined. As a result of the thick outline of the image consisting of, it may not be possible to perform accurate image recognition. Further, even when the area of a specific figure is obtained by image analysis in closed curve information processing, the contour line of the figure becomes thick, and as a result, the correct area cannot be obtained.

The present invention has been made in view of the above problems, and it is mainly to provide an image boundary area detecting apparatus capable of accurately detecting an image boundary area based on pseudo gradation image data. To aim. Another object of the present invention is to provide an image boundary area correction device that can appropriately correct the image boundary area.

[0009]

In order to achieve the above-mentioned object, an image boundary area detecting apparatus according to claim 1 detects an image boundary area which is a portion having a large image density change based on pseudo gradation image data. In the boundary area detection device for an image, an image density difference detection unit for detecting an image density difference between a plurality of pixel areas each of which is composed of a plurality of pixels and is located around the detection target pixel, and the detected image density difference And a boundary area determination unit that determines whether or not the detection target pixel forms a boundary area based on the above.

An image boundary area detecting device according to a second aspect is the image boundary area detecting device according to the first aspect,
The image density difference detection unit is characterized by detecting a difference between the maximum density and the minimum density in the plurality of pixel areas as an image density difference.

According to a third aspect of the present invention, there is provided an image boundary region detecting apparatus according to the first or second aspect, wherein each of the plurality of pixel regions is adjacent to a detection target pixel. And

According to a fourth aspect of the present invention, there is provided an image boundary area correcting device which corrects a boundary area of an image, which is a portion having a large density change of the image based on the pseudo gradation image data, by a spatial filter process using a spatial filter. In the boundary area correction device, an image density difference detection unit that detects an image density difference between a plurality of pixel areas each of which is composed of a plurality of pixels and is located around the detection target pixel, as an image density difference with respect to the detection target pixel, A filter coefficient storage unit that stores each filter coefficient of a plurality of spatial filters that respectively correspond to each image density difference, and the detection detected by the image density difference detection unit for the pseudo gradation image data corresponding to the detection target pixel It is characterized in that spatial filter processing is performed using the filter coefficient stored corresponding to the image density difference with respect to the target pixel.

[0013]

In the image boundary area detecting apparatus according to the first aspect,
In detecting the boundary area of the image, which is the portion where the density difference of the image based on the pseudo gradation image data changes greatly,
An image density difference detection unit detects an image density difference between a plurality of pixel areas each of which is located around the detection target pixel and is composed of a plurality of pixels, based on the number of black pixels in the pixel area. To do. On the other hand, the boundary area determination unit determines whether or not the detection target pixel forms a boundary area based on the detected image density difference. The boundary area is detected by sequentially performing these processes for all the pixels based on the pseudo gradation image data. As described above, in this image boundary area detection device, for each detection target pixel, the detection target pixel configures the boundary area based on the image density difference between the plurality of pixel areas respectively located in the periphery of the detection target pixel. It is determined whether or not to do. For this reason,
Whether or not the boundary area is formed is determined on a pixel-by-pixel basis, and the boundary area can be detected extremely accurately. As a result, for example, when sharpening the boundary area, it is possible to prevent blurring of the image, and to prevent sharpening processing from sharpening even pixels that are not originally needed. Generation of noise due to processing can be significantly reduced. Further, even in the image recognition processing, accurate image recognition can be performed, and even when the area in the boundary region is obtained by closed curve information processing, the area can be obtained accurately.

In the image boundary area detecting device according to the second aspect, the density difference detecting section detects a difference between the maximum density and the minimum density of the plurality of pixel areas as an image density difference. Therefore, the method for detecting the density difference is simple, and the boundary area detection processing can be performed in a short time as compared with the image boundary area detecting device according to the first aspect.

In the image boundary area detecting device according to the third aspect, a plurality of pixel areas for detecting the image density difference are adjacent to the detection target pixel. That is, the density difference detection unit
The density difference between the pixel areas facing each other with one detection target pixel sandwiched between them or the adjacent pixel areas is detected. in this way,
The pixel region moves according to each detection target pixel and is located very close to each detection target pixel. Therefore, as a result of the detected image density difference having extremely effective information as to whether or not the detection target pixel forms a boundary area, the image boundary area detection unit more accurately determines the image boundary area. Can be detected.

In the image boundary area correction device according to the fourth aspect, the image density difference detecting section is the same as the image density difference detecting section according to the first aspect, and the image between the plurality of pixel areas is pixel by pixel. Detect the density difference. When the image density difference is detected, the image boundary area correction device detects the detected image out of the filter coefficients stored in the filter coefficient storage unit for the pseudo gradation image data corresponding to the detection target pixel. Spatial filter processing, that is, processing such as smoothing, emphasizing, and more emphasizing sharpening, is performed using the filter coefficient corresponding to the density difference. In this way, since the boundary area correction processing can be performed in pixel units and according to the image density of each pixel, it is possible to prevent image blurring without damaging the original image and
Since the sharpening process is prevented from sharpening even pixels that are originally unnecessary, it is possible to significantly reduce the generation of noise due to the sharpening process.

[0017]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A pixel density conversion device to which an image boundary area detecting device and an image boundary area correcting device according to the present invention are applied will be described below with reference to the accompanying drawings.

The pixel density conversion device 1 shown in FIG. 1 is used for the pixel density conversion of a facsimile machine.
Density difference detection unit (image boundary area detection device, image density difference detection unit, boundary area determination unit) 2, density difference detection RAM 3, halftone image data conversion unit 4, ROM (filter coefficient storage unit) 5, halftone A conversion RAM 6, a pixel density conversion unit 7, and a pseudo gradation image data conversion unit 8 are provided. The outline of the operation of the pixel density conversion device 1 will be described. Pseudo gradation image data obtained by converting the image data read by the image reading unit (not shown) of the facsimile apparatus into pseudo gradation is transmitted via an interface unit (not shown). When input, the halftone image data conversion unit 4 converts the input pseudo grayscale image data into halftone image data by a spatial filter process using a spatial filter. Then, the pixel density conversion unit 7
Performs pixel density conversion based on the converted halftone image data. Next, the pseudo gradation image data conversion unit 8 converts the converted half tone image data into new pseudo gradation data and outputs it to a communication line (not shown). As a result, the pseudo gradation image data whose pixel density has been converted is transmitted to the receiving side facsimile apparatus having different printing characteristics.

The density difference detection unit 2 is arranged between four pixel regions adjacent to a pixel (detection target pixel, hereinafter referred to as “target pixel”) corresponding to the pseudo gradation image data to be converted into the halftone image data. The maximum density difference (image density difference) is detected. Specifically, the processing content will be described. The density difference detection unit 2 first stores the inputted pseudo gradation image data in the density difference detection RAM 3. Next, when the halftone image data conversion unit 4 converts the pseudo gradation image data into the halftone image data, the density difference detection unit 2, as shown in FIG. The number of black pixel data in the matrix-shaped pixel regions 12a, 12b, 12c, 12d (hereinafter, simply referred to as "pixel region 12" when no distinction is made) is counted, and each count value is counted in each of the pixel regions 12a to 12a. The average density is 12d. Next, the density difference between the average densities of the pixel areas 12a to 12d is obtained, and the maximum value (maximum density difference) is detected. In this case, the maximum density difference is detected in any one of 10 levels from the minimum value of "0" to the maximum value of "9". For example, if the densities of the pixel regions 12a to 12d are
If the values are "8", "7", "4", and "2", respectively, the maximum density difference is "6", which is the value "8" minus the value "2". Next, the density difference detection unit 2 outputs the density difference data indicating the maximum density difference to the halftone image data conversion unit 4. FIG. 3 shows that in the original image of the image shown in FIG. 6, the target pixel 1 determined by the density difference detection unit 2 to have a maximum density difference of not less than “4”, that is, a boundary area
1 indicates that the maximum density difference is detected in pixel units. It should be noted that the maximum density difference of the pixel 56 determined to form the boundary area in the conventional pixel density conversion device 51 is “4” in this embodiment.
Although it is compared with the target pixel 11 described above, as can be seen by comparing both figures, in the present embodiment, the boundary area (contour line) of the image is detected more accurately.

The ROM 5 stores filter coefficients of three types of spatial filters respectively corresponding to the maximum density differences detected by the density difference detection unit 2. In this case, the spatial filter is for converting into multivalued halftone image data based on the density of each pixel in a predetermined pixel area on the original image, and in the present embodiment, it is the same as the image space. The space is composed of a 5 × 5 matrix. In addition, although not particularly limited, in the present embodiment, the maximum density difference 0 to 2, 3
To 6 and 7 to 9, respectively, filter patterns 1 to 3 having filter coefficients shown in Table 1 below.
Correspond to each. Note that the filter pattern 1
Is used to average the image based on the pseudo gradation image data, the filter pattern 2 is used to enhance the boundary region of the image, and the filter pattern 3 is
It is used for sharpening to further emphasize the boundary area.

[0021]

[Table 1]

The halftone image data conversion unit 4 stores the pseudo gradation image data in the halftone conversion RAM 6 and converts the stored pseudo gradation image data into the halftone image data. Specifically, the halftone image data conversion unit 4
The pseudo gradation image data input corresponding to the scanning direction (arrow A in FIG. 4) of the image reading unit is arranged in a pseudo space (see FIG. 4) similar to the transmission image. The RAM 6 for halftone conversion is stored. When converting to halftone image data, the filter coefficient of the spatial filter corresponding to the density difference data output from the density difference detection unit 2 is read from the ROM 5, and the pseudo gradation image data is read based on the filter coefficient. By performing a spatial filtering process on each of the above, the multi-valued halftone image data is converted.

The spatial filter processing will be described in detail. As shown in FIG. 4, the pseudo gradation image data is divided into 5 × 5 matrix pixel areas 13 centered on the target pixel 11, All pixels 14, 14, ... In the blocked pixel area 13 are multiplied by the value of each element of the corresponding spatial filter. If expressed by a mathematical expression, the density of the target pixel 11, the multivalued density of the target pixel 11 indicated by the halftone image data, and the value of each element (a11 to a55 shown in Table 2) of the spatial filter are respectively g (I, j), f (i, j) and M
Assuming that (i, j) (i and j respectively indicate the number of rows and the number of columns of the matrix), the multi-valued density f (i, j) of the target pixel 11 after spatial filtering is given in Table 2 below. It is represented by the formula shown in.

[0024]

[Table 2]

The pixel density conversion unit 7 is configured to be capable of enlarging or reducing the pixel density by an arbitrary magnification by a projection method which is a known means. For example, from the pixel density of 200 dpi or 400 dpi in a G4 facsimile apparatus, 8 × 7.7 pixels / in G3 facsimile machine
Convert to a pixel density of mm ² or 8 × 3.85 pixels / mm ² . In the pixel density conversion processing, since the image data is two-dimensional, linear interpolation is performed from a total of four points in the vertical and horizontal directions.

The pseudo gradation image data conversion unit 8 converts halftone image data into pseudo gradation image data by the error diffusion method. Specifically, the pseudo gradation image data conversion unit 8
For example, when the density of the halftone image data is represented by 128 values, it is determined that the pixel is a black pixel when the density is “64” or more, and is a white pixel when the density is “63” or less. Then, the quantization error, which is the difference between the converted density and the value “64”, is sequentially added to the unconverted pixels around the target pixel 11. In the present embodiment, as the peripheral pixels, the pixel 15 on the right side (scanning direction side) of the target pixel 11, the pixel 16 on the left side by one scan line below, the pixel 17 on the right side of the pixel 16, and the pixels thereof A value obtained by multiplying the quantization error by the coefficients “7/16”, “3/16”, “5/16” and “1/16” is added to the pixel 18 on the right side.
The pseudo-gradation image data conversion unit 8 has all the pixels 14, 1
.. are converted into pseudo gradation image data, and the converted pseudo gradation image data is output to a transmitter (not shown).

Next, the operation of the pixel density conversion device 1 will be described.

When the pseudo gradation image data is input, the density difference detection unit 2 outputs the density difference data for the pixels corresponding to each pseudo gradation image data to the halftone image data conversion unit 4. Next, the halftone image data conversion unit 4 stores the pseudo grayscale image data in the halftone conversion RAM 6, and blocks the stored pseudo grayscale image data in the pixel area 13 centered on the target pixel 11. Turn into. Next, based on the density difference data input from the density difference detection unit 2, the filter coefficient of the spatial filter is read from the ROM 5 and the spatial filter process is executed. As a result, the pseudo gradation image data is converted into halftone image data. Next, the pixel density conversion unit 7 converts the pixel density by performing linear interpolation by the projection method. Next, the pseudo gradation image data conversion unit 8 converts the halftone image data whose pixel density has been converted into new pseudo gradation image data. After that, the pseudo gradation image data is output to a device (not shown) and transmitted to another facsimile device.

As described above, according to the present embodiment, when detecting the boundary area of the image, which is the portion where the density change of the image based on the pseudo gradation image data is large, the density difference detecting section 2 makes the target pixel 11 A maximum density difference in a plurality of pixel regions 12a to 12d adjacent to the
Based on the detected maximum density difference, it is determined whether or not the target pixel 11 constitutes a boundary area. For this reason, the determination is made in pixel units, and the boundary region can be detected extremely accurately. Further, as a result of preventing sharpening processing from sharpening even pixels that are not originally necessary, noise resulting from the sharpening processing is generated only in the pixels forming the detected boundary region. The generation of noise due to the digitization processing can be extremely reduced, and thus the image disturbance can be extremely reduced.

A space having a filter coefficient corresponding to the density difference data detected by the density difference detecting unit 2 when the halftone image data converting unit 4 converts the pseudo gradation image data into the halftone image data. Since the spatial filter processing is performed using the filter, appropriate processing such as smoothing, enhancement, and sharpening is performed according to the degree of density change of the image. As a result, it is possible to reduce the occurrence of blurring and disturbance of the image in the boundary area of the image, and the image after the pixel density conversion is performed without damaging the image of the original image.
The image can be easily viewed.

In this embodiment, the density difference detecting section 2 is
The difference between the minimum value and the maximum value of the average densities of the pixel areas 12a to 12d is the maximum density difference between the plurality of pixel areas, but the present invention is not limited to this and, for example, the middle of the densities of the four pixel areas is used. May be used as the image density difference in the present invention, or after weighting the respective densities of the respective pixel regions 12a to 12d, the difference between the minimum value and the maximum value may be taken as the maximum density difference. .

Further, the filter coefficient of the spatial filter is not limited to the value used in this embodiment, and may be another value. Further, the number of types of spatial filters may be four or more, and it is preferable that more types are provided when increasing the pixel density. Further, when the number of spatial filters is two and the maximum density difference is, for example, a value of "4" or more, the sharpening process is performed assuming that the target pixel 11 forms a boundary region, and the value of "3" is set. In the following cases, the halftone image data conversion unit 4 may be configured to perform the averaging process assuming that the target pixel 11 is a black pixel or a white pixel other than the boundary area.

The input pseudo gradation image data is
It may be pseudo-graded by the tissue dither method, or pseudo-graded by any method such as an error diffusion method and an average error minimization method.

In this embodiment, an example in which the hardware is used has been described, but each processing may be executed by software processing.

[0035]

As described above, according to the image boundary area detecting device of the first aspect, whether or not the detection target pixel constitutes the boundary area is discriminated on a pixel-by-pixel basis. Can be detected. As a result, for example, when sharpening the boundary area, it is possible to prevent blurring of the image and significantly reduce the generation of noise due to sharpening. Further, even in the image recognition processing, accurate image recognition can be performed, and even when the area in the boundary region is obtained by closed curve information processing, the area can be obtained accurately.

According to the image boundary area detecting device of the second aspect, since the density difference detecting section detects the difference between the maximum density and the minimum density of the plurality of pixel areas as the image density difference, the image density difference is detected. The method for detection is simple, and the boundary area detection processing can be performed in a short time as compared with the image boundary area detection device according to the first aspect.

According to the image boundary area detecting device of the third aspect, since the plurality of pixel areas for detecting the image density difference are adjacent to the detection target pixel, the pixel area is defined as each detection target pixel. , And is located very close to each detection target pixel. Therefore, as a result of the detected image density difference having extremely effective information as to whether or not the detection target pixel forms the boundary area, the image boundary area detection unit more accurately determines the boundary area of the image. Can be detected.

According to the image boundary area correction device of the fourth aspect, since the boundary area correction processing is performed on a pixel-by-pixel basis, it is possible to prevent image blurring without impairing the original image. Since the sharpening process is prevented from sharpening even pixels that are originally unnecessary, it is possible to significantly reduce the generation of noise due to the sharpening process.

[Brief description of drawings]

FIG. 1 is a block diagram of a pixel density conversion device according to an embodiment of the present invention.

FIG. 2 is a diagram for explaining a maximum density difference detection process.

FIG. 3 is a diagram showing pixels for which the maximum density difference is determined to be a value “4” or more.

FIG. 4 is a diagram for explaining spatial filter processing.

FIG. 5 is a block diagram of a conventional pixel density conversion device.

FIG. 6 is a diagram showing pixels determined to form a boundary area by a boundary area determination unit in a conventional pixel density conversion device.

FIG. 7 is a diagram for explaining a boundary area determination method of a boundary area determination unit in the conventional pixel density conversion device.

[Explanation of symbols]

 1 Pixel Density Converter 2 Density Difference Detector 4 Halftone Image Data Converter 5 ROM 11 Target Pixel 12 Pixel Area

Claims (4)

[Claims] 1. An image boundary area detecting device for detecting a boundary area of an image, which is a portion where the density change of the image based on the pseudo gradation image data is large, is composed of a plurality of pixels and is located around each pixel to be detected. An image density difference detection unit that detects an image density difference between a plurality of pixel areas, and a boundary area that determines whether or not the detection target pixel constitutes the boundary area based on the detected image density difference. An image boundary area detecting device comprising: a discriminating unit. 2. The image boundary area detection device according to claim 1, wherein the image density difference detection unit detects a difference between maximum density and minimum density in the plurality of pixel areas as the image density difference. . 3. The image boundary area detecting apparatus according to claim 1, wherein each of the plurality of pixel areas is adjacent to the detection target pixel. 4. An image boundary area correction device for correcting a boundary area of an image, which is a portion where the density change of the image based on the pseudo gradation image data is large, by a spatial filter process using a spatial filter. An image density difference detection unit that detects an image density difference between a plurality of pixel regions respectively located around a false detection target pixel as an image density difference with respect to the detection target pixel, and corresponds to each of the image density differences. A filter coefficient storage unit that stores each filter coefficient of a plurality of spatial filters, and the image for the detection target pixel detected by the image density difference detection unit for the pseudo gradation image data corresponding to the detection target pixel An image characterized by performing the spatial filter processing using the filter coefficient stored corresponding to the density difference. Of the boundary region correction device.