JP4149368B2 - Image processing method, image processing apparatus and image forming apparatus, computer program, and computer-readable recording medium - Google Patents

Description

  The present invention uses a local area information of input image data read by a color image input device such as a scanner or a digital still camera to determine whether a pixel belongs to a plurality of areas such as a character area and a halftone area. In particular, the present invention relates to an image processing method that improves the output reproduction accuracy of a document image by switching subsequent processing based on the determination result, and in particular, an image for implementing such an image processing method. The present invention relates to a processing apparatus and an image forming apparatus, and further relates to a computer program for implementing such an image processing method in a general-purpose computer system.

  An image forming apparatus such as a copying machine using an electrophotographic process or an ink jet method is changed to a digital method instead of a conventional analog method, that is, input image data is converted into digital data and output after various processing. Things tend to spread. With the progress of digital image processing technology, full-color digital copiers, multifunction peripherals, printers, and the like that reproduce color images with high image quality have been commercialized.

  Document images copied using these image forming devices include characters, line drawings, photographs, or a mixture of them. In order to obtain a good reproduction image, it conforms to each document type. Image processing must be performed.

  When optimal processing is performed according to the type of document, there is region separation processing in which separation processing is performed on character regions, halftone dot regions, and other (photograph regions) using mask information centered on the pixel of interest. In general, image data read from an image input device such as a scanner is divided into blocks each composed of a plurality of pixels, and is divided into a pixel area, a dot area, and others (photo: photographic paper photograph). It is identified whether it is one of the areas. Thus, whether the image data is a document composed of any one of a character area, a halftone dot area, and other areas (photograph areas), or a document in which these areas are mixed. Is identified, and image processing is performed based on the identification result.

  As an example of the above identification method, Non-Patent Document 1 (“Identification Method of Halftone Photo” (The IEICE Transactions 1987/2 Vol.2 J70-B No.2 p.222 to p.232.)) As shown, image data is identified by the “Block Separate Transformation Method” (BSTM method).

  That is, in Non-Patent Document 1, image data to be subjected to image processing is divided into (m × n) pixel blocks. Next, the image signal level of each pixel in the block is obtained, the maximum value Lmax and the minimum value Lmin of the image signal level are determined in the block, and the value of the difference Lmax−Lmin is obtained. Thereafter, the value of the difference Lmax−Lmin is compared with a predetermined reference value Q. Based on the comparison result, a block satisfying Lmax−Lmin <Q is determined as a photographic region.

For a block determined to satisfy Lmax−Lmin ≧ Q, an average value of image signal levels obtained for each pixel in the block is calculated, and each pixel is binarized based on the average value. Specifically, a pixel having an image signal level less than the average value is converted to “0”, and a pixel having an image signal level equal to or higher than the average value is converted to “1”. Then, determine the number K _H of change in occurs in between the pixels consecutive in the main scanning direction in the block "0", "1". Similarly, for the sub-scanning direction in the block, the number of times K _V of “0” and “1” changes between successive pixels is obtained. Thereafter, the number of changes K _H · K _V is compared with a predetermined reference value T, and a block where K _H ≧ T and K _V ≧ T is determined as a halftone dot region, and K _H <T or K _V A block where <T is determined as a character area.

  However, in the above determination, an erroneous determination may occur due to an excess or shortage of information when determining a character area, a dot area, or a photograph area. When image processing is performed based on the identification result obtained by such erroneous determination, erroneous image processing is performed, which causes deterioration of the output image such as loss of continuity of the output image. Therefore, the following correction processing is performed on the above identification result.

  First, when determining a character region, erroneous determination is likely to occur at the end portion of the character stroke. A misjudgment in the character stroke causes the character to be crushed and causes a significant deterioration in the quality of the reproduced image. Therefore, in the three blocks that are continuous in the main scanning direction or the sub-scanning direction, if it is determined that both of the outer blocks are character areas, the intermediate block sandwiched between these outer blocks is also the character area. Is corrected so as to be determined. Thereby, the determination precision of a character area can be improved.

Further, in the coarse halftone dot area, the image quality is deteriorated because the identification accuracy of the halftone dot area is low. Therefore, two values are set as the reference value T when the number of changes K _H · K _V described above is compared with the reference value T. Then, a value to be used as the reference value T is selected according to the number of blocks determined to be a halftone dot area from among the blocks existing in the vicinity of the target block, and it is determined whether or not it is a halftone dot area. I do. Thereby, it can correct | amend so that identification accuracy may improve with respect to identification of a halftone dot area | region, and can improve the image quality of a halftone dot area | region.

  According to the correction for the halftone area, the accuracy of identifying the halftone area can be improved. On the other hand, the number of blocks erroneously determined as the halftone area in the character area increases. Therefore, a block in which the average value of the image signal level is rapidly changed is extracted as a contour portion of the character region, and the character region is accurately detected.

As described above, in the conventional image processing method described above, determination is made for each block of (m × n) pixels when performing determination on components such as a character region, a halftone dot region, and a photographic region. Is going. That is, with this block as a unit, the value of the difference Lmax−Lmin and the number of changes K _H · K _V are calculated in the block to determine which component each block corresponds to. is doing. In addition, by adding further feature quantities such as extraction of the determination result of the block located around the block of interest and the outline of the character area, the determination accuracy in the halftone area and the character area is improved. ing.

Further, in Patent Document 1 (Japanese Patent Laid-Open No. 4-180350), it is determined whether or not a halftone pixel is included in the target pixel by pre-scanning. By ignoring the separation means, the separation accuracy of the character part is improved.
Japanese Patent Laid-Open No. 4-180350 (released on June 26, 1992) Identification method of halftone dot photo (The IEICE Transactions 1987/2 Vol.2 J70-B No.2 p.222-p.232.) Proceedings of IEICE Technical Committee 90-06-04 (1990)

  By the way, since the halftone dot area generally exists in a wider area than the character area, it is advantageous to use information of a wide area for the determination of the halftone dot area. In particular, in a halftone dot region having a low number of lines, that is, a halftone dot region having a coarse mesh, it is necessary to use information on a wider area in order to detect the halftone dot region with high accuracy. That is, in order to improve the detection accuracy of the halftone dot region, it is necessary to use information in a wider area in order to obtain more information.

  On the other hand, since the character area exists in a narrower area than the halftone dot area, it is not necessary to obtain information from a wider area in order to obtain more information.

  Therefore, in the conventional region separation methods disclosed in Non-Patent Document 1 and Patent Document 1 described above, the input image is supplied in parallel to the halftone dot region detection unit and the character region detection unit. Information in the same area is used for detecting the character area.

  Thus, when using information in the same area, the amount of information necessary to detect a halftone dot area existing in an area larger than the character area is insufficient, and sufficient detection accuracy can be obtained. As a result, there arises a problem that the output image is deteriorated.

  The present invention has been made in view of the above-described problems, and its purpose is to accurately detect areas having different areas of character areas and halftone dot areas, and to reduce deterioration of an output image. An image processing method, an image processing apparatus, an image reading apparatus, an image forming apparatus, and a computer program to be executed by a general-purpose computer system are provided.

  The inventors of the present application pay attention to the fact that the halftone dot region generally exists in a wide area, and it is advantageous to use information in a wide area for detection, and detection processing is performed on an input image with low resolution. By doing so, it has been found that by using a pseudo-wide area, the amount of information necessary for halftone dot detection can be increased and the detection accuracy of the halftone dot region can be increased.

  That is, in the image processing method according to the present invention, input image data is input to a region separation processing unit that performs processing for identifying a plurality of regions such as a character region and a halftone dot region, and the region separation processing unit. A resolution conversion unit that converts the resolution of the input image data, and a control unit that controls the resolution conversion unit so as to convert the resolution of the input image data in accordance with the target region identified by the region separation processing unit are provided. It is characterized by being.

  The region separation processing unit includes at least character detection means for detecting a character and halftone dot detection means for detecting a halftone dot region, and the control unit is configured to input image data input to the character detection means. The resolution conversion unit is controlled so that the resolution differs from the resolution of the input image data input to the halftone dot detection means.

  The control unit controls the resolution conversion unit so that the resolution of the input image data input to the halftone dot detection unit is lower than the resolution of the input image data input to the character detection unit. It is said.

  Furthermore, a halftone dot number calculating unit for recognizing the number of halftone dot lines is provided, and the control unit is configured to input image data to be input to the halftone dot detecting unit according to the number of lines recognized by the halftone dot number calculating unit. The resolution converter is controlled so as to switch the resolution.

  An image forming apparatus according to the present invention includes the image processing apparatus having the above-described configuration.

  An image processing method according to the present invention is a resolution for converting the resolution of input image data in an image processing method comprising an area separation process step for identifying a plurality of areas such as a character area and a halftone dot area for input image data. A conversion step, and in the region separation processing step, a resolution conversion step of changing the resolution of the input image data in accordance with a region to be identified is provided.

  The region separation processing step includes at least a character detection step for detecting a character and a halftone dot detection step for detecting a halftone dot region, and the resolution conversion step includes the character detection step and the halftone dot detection step. The feature is that the resolution of the input image data is different.

  Further, a halftone dot number calculating step for recognizing the number of halftone dot lines is provided, and the resolution converting step sets a resolution according to the number of lines recognized by the halftone dot number calculating step, and switches according to a region to be identified. It is characterized by.

  A computer program according to the present invention is a computer program for causing a computer to execute a region separation process for identifying a plurality of regions such as a character region and a halftone region, and the resolution of input image data when the computer separates the region. The method includes a procedure for differentiating the values depending on the area to be identified.

  A computer program according to the present invention is a computer program for causing a computer to execute an area separation process for identifying a plurality of areas such as a character area and a halftone dot area. The resolution is set by recognizing the number of lines of halftone dots, and includes a procedure for changing the resolution depending on a region to be identified.

  The computer program may be stored in a computer so as to be executable.

  The image processing apparatus according to the present invention is provided with a control unit that controls the resolution conversion unit so as to convert the resolution of the input image data in accordance with the target region to be identified by the region separation processing unit. Depending on the area, the resolution of the input image data input to the area separation processing unit can be varied.

  In general, in region separation processing, a plurality of types of regions (character region, halftone region, and photo region) having different properties are classified using the density information of local blocks.

  Therefore, in order to classify a region, separation is performed based on a signal input as a predetermined local block. However, since the resolution suitable for detection differs depending on the type of detection, depending on the type of detection. By varying the input resolution, it is possible to improve the region separation accuracy in the region separation processing unit, that is, the detection accuracy of each region.

  Specifically, the region separation processing unit includes at least a character detection unit that detects a character and a halftone dot detection unit that detects a halftone dot region, and the control unit receives an input input to the character detection unit. By controlling the resolution conversion unit so that the resolution of the image data and the resolution of the input image data input to the halftone dot detection means are different, the optimum input in both the character detection means and the halftone dot detection means Detection can be performed using a signal, and detection accuracy of a region can be increased.

  Usually, high resolution input image data is suitable for detecting fine lines in a character area. On the other hand, detection of a halftone dot region is more suitable for low resolution input image data because detection accuracy is higher when detection is performed by putting a wave of a signal in the widest possible range in a local mask.

  Therefore, the control unit controls the resolution conversion unit so that the resolution of the input image data input to the halftone dot detection unit is lower than the resolution of the input image data input to the character detection unit. Thus, it is possible to increase the detection accuracy of the region.

  In this resolution conversion, the resolution of the input image data is left as it is without performing the resolution conversion on the character detection means, and the resolution conversion is performed only on the halftone dot detection means to change the resolution of the input image data. Although it is possible to reduce the resolution, the resolution detection is performed on the character detection unit to make it higher than the resolution of the input image data, and the input image data is not converted on the halftone detection unit without performing the resolution conversion. It is also possible to keep the resolution of. Further, the resolution may be converted for both means.

  Furthermore, in order to increase the detection accuracy of the halftone dot region, the resolution may be converted according to the number of lines of the halftone dot.

  Specifically, the control unit controls the resolution conversion unit to switch the resolution of the input image data input to the halftone dot detection unit according to the number of lines recognized by the halftone line number calculation unit. Thus, it is possible to switch the input resolution to the halftone detection means as appropriate according to the number of lines of the halftone dot, and it is possible to perform halftone dot detection with higher accuracy using the optimum input resolution.

  By providing the image processing apparatus having the above configuration, the region separation accuracy of the input image data is improved, so that a high-quality image can be output.

  The computer program according to the present invention includes a procedure for varying the resolution of the input image data depending on the area to be identified when the computer separates the areas, thereby reducing the input resolution between character detection and halftone dot detection. Differently, the computer can read and execute an image processing method that can improve the detection accuracy of the region, and this image processing method can be general-purpose.

  Further, according to the computer program according to the present invention, the resolution of the image data input when the computer separates the areas is set by recognizing the number of lines of halftone dots, and varies depending on the area to be identified. By including the image processing method, it becomes possible to set a more appropriate input resolution in accordance with the number of lines of halftone dots, and the computer uses an image processing method that can detect halftone dots with higher accuracy using the optimum input resolution. The image processing method can be general-purpose.

  When the above computer program is stored in a computer-executable recording medium, an image processing method capable of increasing detection accuracy by changing the input resolution between character detection and halftone dot detection, or the number of lines of halftone dots Accordingly, it is possible to set a more appropriate input resolution in accordance with the image processing, and it is possible to easily supply a computer program for an image processing method capable of detecting halftone dots with higher accuracy using the optimum input resolution.

[Embodiment 1]
The embodiment of the present invention will be described as follows.

  FIG. 1 is a block diagram showing a configuration of an image processing apparatus (color image processing apparatus) according to the present invention. In the present embodiment, a digital color copying machine will be described as an example of an image forming apparatus.

  As shown in FIG. 1, the color image processing apparatus 1 includes an A / D (analog / digital) conversion unit 2, a shading correction unit 3, an input tone correction unit 4, a color correction unit 7, and a black generation and under color removal unit 8. , A spatial filter processing unit 9, an output tone correction unit 10, a tone reproduction processing unit 11, and an operation panel 12. A color image input device 13 and a color image output device 14 are connected to the color image processing apparatus 1 and constitute a digital color copying machine as a whole.

  Further, the color image processing apparatus 1 includes a resolution conversion unit 51, a region separation processing unit 5, a control unit 52, and a memory 53 between the input tone correction unit 4 and the color correction unit 7.

  The color image input device 13 (image reading means) is composed of, for example, a scanner unit equipped with a CCD (Charge Coupled Device), and the reflected light image from the original is converted into RGB (R: red, G: green, B: blue). ) Analog signals are read out by the CCD, and RGB analog signals are output to the color image processing apparatus 1.

  The analog signal read by the color image input device 13 passes through the color image processing device 1 through the A / D conversion unit 2, the shading correction unit 3, the input tone correction unit 4, the color correction unit 7, and the black generation signal. The color removal unit 8, the spatial filter processing unit 9, the output gradation correction unit 10, and the gradation reproduction processing unit 11 are sent in this order, and C (cyan), M (magenta), Y (yellow), and K (black) digital The color signal is output to the color image output device 14.

  The signal output from the input tone correction unit 4 is output to the resolution conversion unit 51 in addition to the color correction unit 7. The resolution conversion unit 51 converts the resolution of the input image data and outputs the converted image data to the subsequent region separation processing unit 5. Details of this resolution conversion will be described later.

  The A / D conversion unit 2 converts RGB analog signals into digital signals, and the shading correction unit 3 applies a color image to the digital RGB signals sent from the A / D conversion unit 2. Processing for removing various distortions generated in the illumination system, imaging system, and imaging system of the input device 13 is performed.

  The input tone correction unit 4 treats the RGB signal (RGB reflectance signal) from which various distortions have been removed by the shading correction unit 3 as a density signal or the like as an image processing system employed in the color image processing apparatus 1. Convert to an easy signal. At the same time as adjusting the color balance, image density adjustment processing such as removal of background density and contrast is performed.

  The resolution conversion unit 51 operates when detecting a halftone dot, and converts the resolution of the RGB signal from the input tone correction unit 4. Here, it is preferable to use image data read by pre-scanning by the color image input device 13 as data used for resolution conversion. However, scanning under normal conditions (main scanning), A / D conversion, The shading-corrected image data may be temporarily stored in an image memory such as a hard disk, and resolution conversion (lower resolution) may be performed on the image data read from the image memory.

  Further, as a resolution conversion method performed in the resolution conversion unit 51, a nearest neighbor method, a bilinear method, a bicubic method, or the like can be used.

  The nearest neighbor method is a method in which the color of the image composing point closest to the interpolation point is used as it is as the color of the interpolation point, and is the simplest enlargement / reduction (resolution conversion) algorithm among the above three methods. is there. That is, in the nearest neighbor method, the image is enlarged so that each point of the original image is spread as a square tile.

  The advantages of this method are that high-speed processing is possible and that the contrast of the image is not lost.

  The bilinear method is a method in which the weighted average value of the colors of the four image composing points around the interpolation point is used as the color of the interpolation point, and a smooth enlarged image with higher quality than the nearest neighbor method can be obtained.

  The bicubic method is a method in which the result of interpolating 4x4 = 16 image constituent points around the interpolation point by the cubic spline method is used as the color of the interpolation point, which is considerably smoother than the above two types. An enlarged image can be obtained.

  In the present embodiment, the nearest neighbor method is adopted as the resolution conversion method performed by the resolution conversion unit 51 in view of the lightness of processing and the balance of accuracy.

  It should be noted that the resolution converter 51 may not operate (through processing) when detecting other than halftone dots from the viewpoint of power consumption or the like, but may operate when detecting other than halftone dots. Good.

  Control of the operation of the resolution conversion unit 51 is performed by a control unit 52 (for example, a CPU) connected to the resolution conversion unit 51. When pre-scan data is used, resolution conversion is performed on image data in the main scanning direction on the premise that the resolution in the sub-scanning direction is normally set lower than the resolution at the time of main scanning in order to increase the speed. I do. Alternatively, the resolution conversion unit 51 may be provided before the halftone dot determination unit (described later) provided in the region separation processing unit 5.

  The region separation processing unit 5 separates each pixel in the input image into one of a character region, a halftone dot region, and a photograph (photographic paper photograph) region based on the RGB signal input through the resolution conversion unit 51. Is. Based on the separation result, the region separation processing unit 5 sends a region identification signal indicating to which region the pixel belongs to the black generation and under color removal unit 8, the spatial filter processing unit 9, and the gradation reproduction processing unit 11. It is designed to output. Here, the region separation processing unit 5 is connected to a memory 53 for holding the halftone dot determination result obtained by the region separation processing unit 5 for the image data subjected to resolution conversion. The memory 53 functions when holding the halftone dot determination result once obtained using image data read by pre-scanning or image data read from an image memory such as a hard disk. This is not necessary for real-time processing that performs character detection and halftone detection on resolution-converted image data.

  The color correction unit 7 performs a process of removing color turbidity based on the spectral characteristics of the CMY color material including unnecessary absorption components in order to realize faithful color reproduction.

  The black generation and under color removal unit 8 generates a black signal for generating a K signal from the CMY three-color signals after color correction, and generates a new CMY signal by subtracting the K signal obtained by the black generation from the original CMY signal. The processing is performed. In other words, the black generation and under color removal unit 8 changes the CMY three-color signal to the CMYK four-color signal.

  The spatial filter processing unit 9 performs spatial filter processing using a digital filter on the image data of the CMYK signal input from the black generation and under color removal unit 8 to correct the spatial frequency characteristics. Thus, processing is performed so as to prevent blurring of the output image and deterioration of graininess.

  The output tone correction unit 10 performs output tone correction processing for converting a signal such as a density signal into a halftone dot area ratio that is a characteristic value of the color image output device 14.

  The gradation reproduction processing section 11 performs gradation reproduction processing for processing the image data of the CMYK signal based on the area identification signal so that the image is finally separated into pixels and each gradation can be reproduced. (Halftone generation).

  The operation panel 12 is a setting button for controlling the operation of the display unit such as a liquid crystal display and the entire digital color copying machine (for example, an image mode (character mode / character photo mode / photo mode, etc.) indicating a document type to be copied. ) Is set).

  The image data subjected to the above-described processes is temporarily stored in a storage unit (not shown), read out at a predetermined timing, and input to the color image output device 14. Is controlled by a CPU (Central Processing Unit) (not shown).

  Here, an image processing method by the digital color copying machine having the above configuration will be described.

  First, a pre-scan is executed. That is, the color image input device 13 reads the image data of the document as RGB analog signals and outputs them to the color image processing device 1.

  The color image processing apparatus 1 converts an input analog signal into an RGB digital signal by the A / D conversion unit 2 and outputs the RGB digital signal to the shading correction unit 3. The shading correction unit 3 removes various distortions generated in the illumination system, the imaging system, and the imaging system of the color image input device 13 from the input RGB digital signal. 4 is input. The input tone correction unit 4 adjusts the color balance of the input RGB signal and simultaneously performs image quality adjustment processing such as removal of background density and contrast, and then outputs the result to the subsequent color correction unit 7 and resolution conversion unit 51. In this pre-scan, the color correction unit 7 does not operate and the resolution conversion unit 51 operates.

  The resolution conversion unit 51 performs resolution conversion on the image data that has been subjected to the input gradation correction processing by the input gradation correction unit 4.

  Then, the region separation processing unit 5 extracts a halftone dot region from the image data whose resolution has been converted by the resolution conversion unit 51, and stores the result in the memory 53.

  Next, by the main scan, the image data is read again, and A / D conversion / shading correction and input gradation correction processing are performed. Alternatively, image data stored in a hard disk or the like is read and input tone correction processing is performed. At this time, the image data subjected to the input gradation correction processing by the input gradation correction unit 4 is output to the subsequent color correction unit 7 and the resolution conversion unit 51 as in the pre-scan. In 51, resolution conversion processing is not performed.

  Then, the region separation processing unit 5 separates each pixel in the input image into either a character region or a photograph region (or other region) from the input RGB signal, and reads the halftone determination result from the memory, The identification signal is output to the black generation and under color removal unit 8, the spatial filter processing unit 9, and the gradation reproduction processing unit 11.

  In the color correction unit 7, the RGB signal from the input tone correction unit 4 is subjected to processing for removing color turbidity based on the spectral characteristics of the CMY color material including unnecessary absorption components, and then the CMY signal after color correction is processed. Output to the black generation and under color removal unit 8

  The black generation and under color removal unit 8 performs black generation for generating a K signal from the input CMY signal and processing for generating a new CMY signal by subtracting the K signal from the input CMY signal. The CMY three-color signal is converted into a CMYK four-color signal and then output to the spatial filter processing unit 9. Here, based on the region identification signal from the region separation processing unit 5, a CMYK signal suitable for each region is generated.

  As an example of the black generation processing, there is a method (general method) for generating black by UCR (under color removal processing). In this method, the input / output characteristic of the black generation curve is y = f (x), the input data is C, M, Y, the output data is C ′, M ′, Y ′, K ′, UCR (Under When the color removal rate is α (0 <α <1), the black generation and under color removal processing is expressed by the following equations (1) to (4).

上記空間フィルタ処理部９は、入力したＣＭＹＫ信号の画像データに対して、領域識別信号に基づいてデジタルフィルタによる空間フィルタ処理を行い、空間周波数特性を補正することによって出力画像のぼやけや粒状性劣化を防ぐように処理する。また、階調再現処理部１１は、空間フィルタ処理部９と同様に、ＣＭＹＫ信号の画像データに対して、領域識別信号に基づいて所定の処理を施す。

The spatial filter processing unit 9 performs spatial filter processing using a digital filter on the input image data of the CMYK signal based on the region identification signal, and corrects the spatial frequency characteristics, thereby blurring the output image and degrading graininess. Process to prevent. Further, similar to the spatial filter processing unit 9, the gradation reproduction processing unit 11 performs predetermined processing on the image data of the CMYK signal based on the region identification signal.

  For example, a region separated into characters by the region separation processing unit 5 is subjected to a sharp enhancement process in a spatial filter by the spatial filter processing unit 9 in order to improve the reproducibility of a black character or a color character, in particular, and a high frequency enhancement amount. Is increased. At the same time, the gradation reproduction processing unit 11 selects binarization or multi-value conversion on a high-resolution screen suitable for high-frequency reproduction.

  Further, with respect to the region separated into halftone dot regions by the region separation processing unit 5, the spatial filter processing unit 9 performs low-pass filter processing for removing the input halftone dot component. The output tone correction unit 10 performs output tone correction processing for converting a signal such as a density signal into a halftone dot area ratio that is a characteristic value of the color image output device 14, and then the tone reproduction processing unit 11. Then, gradation reproduction processing (halftone generation) is performed so that the image is finally separated into pixels and each gradation is reproduced.

  In addition, regarding the region separated into the photograph by the region separation processing unit 5, binarization or multi-value processing is performed on the screen with an emphasis on gradation reproducibility.

  A CMYK signal obtained by performing a series of image processing on the image data input from the color image input device 13 by the color image processing device 1 described above is output to the color image output device 14.

  The color image output device 14 outputs to the paper based on the input CMYK signal.

  Here, a method of separating the image into one of a character region, a halftone dot region, and a photograph region (or other region) in the region separation processing unit 5 will be described below with reference to FIGS.

  FIG. 2 is a block diagram showing a schematic configuration of the region separation processing unit 5, and FIG. 3 is a conceptual diagram conceptually showing a flow of the region separation processing.

  As a method of separating input image data into text, halftone dots, photographic regions, etc. in the region separation processing unit 5, it is described in, for example, Non-Patent Document 2 (“Image Electronics Society of Japan Proceedings 90-06-04”). Can be used. Details will be described below. The following determination is performed within a block of M × N (M 1 and N are natural numbers) pixels centered on the pixel of interest, and this is used as the region identification signal of the pixel of interest.

  Further, an average value (Dave) of signal levels is obtained for the pixels in the block, and each pixel in the block is binarized using the average value. Further, the maximum pixel signal level (Dmax) and the minimum pixel signal level (Dmin) are also obtained at the same time.

  In order to realize the region separation processing, the region separation processing unit 5 includes a character detection unit 20 that detects a character region, a halftone detection unit 21 that detects a halftone region, as shown in FIG. Based on the detection results of these detection units, a determination unit 22 that determines either a character region or a halftone dot region is provided.

  Since the difference between the maximum signal level and the minimum signal level is large and the density is considered high in the character area, the character detection unit 20 identifies the character area as follows. The maximum and minimum signal levels and their difference (Dsub) are compared with threshold values PA 1, PB 2 and PC, and if one of them is exceeded, a character area is determined.

That is, in the character detection unit 20, the signal level is
Dmax> PA or Dmin> PB or Dsub> PC
If either of the relations is satisfied, the character area is set.

On the other hand, in the halftone dot region, the halftone dot detection unit 21 performs the halftone dot region as follows using the fact that the fluctuation of the image signal in the small region is large and the density is higher than the background. With respect to the binarized data, the number of change points from 0 to 1 in the main scanning and sub-scanning directions is obtained, and the number of change points from 1 to 0 is obtained as K _H and K _V , respectively, and the thresholds T _H and T _{V are obtained.} If both of them exceed the threshold, the halftone dot region is set. In order to prevent the erroneous determination of the background, comparing the previously determined Dmax, Dmin, threshold Dave _B 1, _{B 2} and.

When the following relational expression is satisfied, the halftone dot detection unit 21 determines that it is a halftone dot region.
Dmax−Dave> B ₁ ,
And, Dave -Dmin> _B 2,
And K _H > T _H ,
And K _V > T _V
The determination unit 22 outputs a region identification signal based on the detection results of the character detection unit 20 and the halftone detection unit 21. Here, the determination unit 22 outputs a region identification signal according to Table 1 shown below.

この表１において、文字検出部で文字、網点検出部で網点と判定された時を「１」、そうでない時を「０」で表している。網点領域でも文字領域でもない領域の画素はその他（写真）領域とする。以上によって、文字領域・網点領域・その他（写真領域）に分離することができる。

In Table 1, “1” is indicated when the character is determined by the character detection unit and halftone dot is determined by the halftone detection unit, and “0” is indicated otherwise. Pixels in a region that is neither a halftone dot region nor a character region are defined as other (photo) regions. As described above, it can be separated into a character area, a halftone dot area, and the other (photo area).

  The creation of the region identification signal in the determination unit 22 is not limited to using the result shown in Table 1 above, and other methods may be used. For example, if the character detection method of the character detection unit 20 and the halftone detection method of the halftone detection unit 21 are different from those described above, the determination operation in the determination unit 22 is also different from the above.

  In the present embodiment, the region separation processing unit 5 is input with the resolutions different between the character detection unit 20 and the halftone detection unit 21 in the region separation processing unit 5 via the resolution conversion unit 51 described above. It has come to be. Here, an RGB signal without resolution conversion is output to the character detection unit 20, while an RGB signal subjected to resolution conversion (low resolution conversion) is output to the halftone detection unit 21. ing. Thereby, even if the same RGB signal is input to the resolution conversion unit 51, the resolution of the RGB signal input to the halftone detection unit 21 is lower than the resolution of the RGB signal input to the character detection unit 20. ing.

  Since the resolution of the RGB signal input to the halftone detection unit 21 only needs to be lower than the resolution of the RGB signal input to the character detection unit 20, the resolution of the input RGB signal is the halftone detection unit 21. Not only that, the character detection unit 20 may perform the conversion.

  In this embodiment, as shown in FIG. 3, it is assumed that a resolution-converted signal is input only for halftone dot detection. Note that the dot detection and the character detection in the region separation processing unit 5 are performed simultaneously, and are performed for all the pixels.

  As described above, by reducing the resolution of the signal input to the halftone detection unit 21, the detection accuracy of the halftone area in the halftone detection unit 21 can be improved.

  Hereinafter, an effect of resolution conversion in halftone dot detection will be described.

  The present invention is characterized in that the input resolution to the character detection unit 20 and the halftone detection unit 21 in the region separation processing unit 5 is made different in order to improve the performance of the region separation processing.

  In particular, in halftone dot detection, the resolution of the input image data is lowered to obtain more information, thereby improving the detection accuracy of the halftone area. For example, as shown in FIG. 4, when the resolution of the input image data is 600 dpi and the number of lines is 133, when detecting halftone dots without performing resolution conversion, information necessary for detecting halftone dots Is indicated by a circle, the number is eight, but when resolution conversion is performed and the halftone dots are detected by reducing the resolution to 300 dpi, the same size as when halftone dots are detected without performing resolution conversion The number of circles as information necessary for detecting halftone dots in the mask is 25.

  Therefore, by reducing the resolution of the input image data, the number of circles necessary for halftone dot detection, that is, the amount of information can be increased, so that the halftone dot detection accuracy can be improved.

  In this manner, reducing the resolution greatly enlarges the area for detecting the halftone dots, and thus is very advantageous when the image is present in a wide area such as a halftone dot.

  Differences in the detection accuracy of the halftone dot region with and without resolution conversion will be described below with reference to FIGS. 5 (a) to 5 (f) and FIGS. 6 (a) to 6 (f).

  In FIGS. 5A to 5F and FIGS. 6A to 6F, the numerical values on the vertical axis and the horizontal axis represent coordinates. FIGS. 5A to 5C and FIGS. 6A to 6C show the results of the halftone dot detection for the input image corresponding to the character area, and FIGS. 5D to 5F. 6D to 6F show halftone dot detection results for an input image corresponding to a halftone dot area.

  FIGS. 5 (a) (d) and 6 (a) (d) are images obtained by subjecting an image read by the color image input device 13 to A / D conversion / shading correction and input tone correction processing. It is. FIGS. 5A and 5D are 600 dpi (dot per inch) input images, and FIGS. 6A and 6D are 300 dpi obtained by performing resolution conversion on the 600 dpi input image by a nearest neighbor. Input image.

FIGS. 5B and 6E and FIGS. 6B and 6E show the smaller value of K _H and K _V calculated by the halftone dot detection method as the concentration. Therefore, the larger this value (the higher the density), the higher the possibility of halftone dots. 5 (c) (f) and 6 (c) (f) are binarized by performing threshold processing on the values of FIGS. 5 (b) (e) and 6 (b) (e). It is a result. A white part indicates an area detected as a halftone dot, and a black part indicates an area not detected as a halftone dot.

  In order to perform the comparison by matching the conditions at the respective input resolutions using the halftone dot detection method, first, a 600 dpi image (FIG. 5A) and a 300 dpi image (FIG. 6A) of the same character image serving as a reference are used. )) Is prepared, and a threshold value is set for this reference character image so that halftone dot detection is not erroneously detected. In other words, if binarization processing is performed using this threshold value, as shown in FIGS. 5C and 6C, the halftone dot detection rate is 0%, that is, both are erroneously detected as halftone dots. Does not exist.

  Next, using the threshold values set as described above, the same halftone image, that is, the image shown in FIG. 5D (600 dpi image) and the image shown in FIG. 6D ((600 dpi). As a result of performing a halftone dot detection process on the input and an image whose resolution has been converted to 300 dpi using a nearest neighbor), as shown in FIG. 5F, halftone dot detection is performed in a 600 dpi input image. Although the rate was 48.7%, as shown in FIG. 6F, the halftone dot detection rate of the 300 dpi input image was 74.8% in the same halftone dot detection algorithm.

  Therefore, in halftone dot detection, when the halftone dot detection mask is a fixed block of M × N (M and N are natural numbers) pixels centered on the target pixel, lower resolution may be advantageous. I can confirm. On the other hand, since character detection needs to increase the resolution in order to detect the detailed part of the character, supplying different input resolutions to the character detection unit and the halftone dot detection unit increases the detection accuracy of the region separation. It is very effective. In other words, in order to increase the accuracy of area recognition, it is necessary to increase the resolution of character detection and to decrease the resolution of halftone detection.

  In the second embodiment described below, a description will be given of using information that recognizes the number of lines of a halftone dot image in order to further improve the accuracy of halftone dot detection.

[Embodiment 2]
Another embodiment of the present invention will be described as follows. In the present embodiment, members having the same functions as those in the first embodiment are denoted by the same reference numerals, and description thereof is omitted.

  As shown in FIG. 7, the digital color copying machine according to the present embodiment is a color image in which a halftone line number calculation unit 71 is provided with respect to the digital color copying machine shown in FIG. The difference is that a processing device 61 is provided.

  As shown in FIG. 8, the halftone line number calculation unit 71 is provided in the preceding stage of the resolution conversion unit 51, and calculates the halftone line number from the RGB signals from the input tone correction unit 4 (FIG. 7). It has become.

  The relationship between the halftone line number calculation unit 71 and the resolution conversion unit 51 is, for example, as shown in FIG. That is, the input image data subjected to the input tone correction processing is recognized by the line number recognition unit (halftone line number calculation unit 71), and the resolution is set so as to obtain an appropriate resolution based on the recognition result. The resolution is converted by the conversion unit (resolution conversion unit 51), and halftone detection is performed using the input image data subjected to the resolution conversion, thereby improving the halftone detection accuracy.

  In other words, the accuracy of halftone dot detection is improved by performing optimum resolution conversion according to the number of lines in the halftone image.

  Here, the difference between the resolution and the determination result with respect to the number of lines of the input image will be described below with reference to FIGS.

  The determination method uses the above-described halftone dot detection algorithm, and each resolution conversion is performed by a nearest neighbor method using a 600 dpi input image as a reference character original image.

  That is, using the reference character document shown in FIG. 10A, a threshold value is set to such an extent that no character is erroneously detected, and a halftone dot detection rate obtained as a result of halftone dot detection using this set threshold value. Is shown in FIG.

  According to the result shown in FIG. 10B, it can be seen that the detection rate of the lower resolution increases as the number of lines of the input document becomes coarse (the number of lines decreases). Therefore, it is very effective to discriminate the number of lines of an input document (input image) in advance by line number recognition described later, and to supply an image converted to a resolution corresponding to the number of lines to the halftone detection unit. I understand.

  Here, the operation flow of the area separation process in the digital color copying machine having the above configuration will be described below with reference to FIGS.

  First, the halftone line number calculation unit 71 performs a halftone line number calculation process on the input image data (step S1). Here, the resolution of the input image data is 600 dpi.

  Next, the resolution conversion unit 51 converts the input image data into a resolution corresponding to the number of halftone lines calculated in step S1 (step S2). Here, if the number of halftone lines calculated in step S1 is low (65 to 85 lines), the process proceeds to step S3, and the resolution is converted from 600 dpi to 150 dpi. If the number of halftone lines calculated in step S1 is the number of intermediate lines (120 to 133 lines), the process proceeds to step S4, and the resolution is converted from 600 dpi to 300 dpi. Further, if the number of halftone lines calculated in step S1 is a high number of lines (150 lines or more), the process proceeds to step S5, and the resolution is converted from 600 dpi to 400 dpi.

  Subsequently, the halftone dot detection unit 21 of the region separation processing unit 5 performs a halftone dot region determination process from the input image data subjected to resolution conversion (step S6).

  Next, input image data that has not been subjected to resolution conversion processing is read to the character detection unit 20 of the region separation processing unit 5 (step S7).

  Next, the character detection unit 20 performs a character area determination process from the input image data (step S8).

  The above series of processing (step S1 to step S8) is performed on the entire image. As described above, the halftone dot area determination process from S1 to S6 and the character area determination process from S8 may be performed in parallel.

Here, the dotted line number calculation process performed in step S1 will be described. As a method for calculating the number of halftone lines, for example, the following method is possible. A mask of m × n pixels is cut out centering on an arbitrary pixel of the input image data. However, in order to use FFT (Fast Fourier Transform) as a frequency analysis process for this cut out mask, normally, m, n: 2 ^k
It is preferable to set as In this embodiment, m = 16, n = 16, that is, k = 4 is used as an example, but the present invention is not limited to this.

  The mask of m × n = 16 × 16 pixels cut out with the target pixel as the center in this way is expressed by the following formula (5), for example, as shown in FIG.

次に、切り出されたマスクに対してFFT 処理が行なわれ、実空間領域から周波数領域への変換が行なわれて、図１２（ｂ）に示すような２次元スペクトル配置とされる。この際の変換式は下記式（６）にて一般的に与えられる。

Next, FFT processing is performed on the cut out mask, conversion from the real space region to the frequency region is performed, and a two-dimensional spectrum arrangement as shown in FIG. 12B is obtained. The conversion formula at this time is generally given by the following formula (6).

このフーリエ変換の振幅スペクトル強度を用いることによって上記マスクごとに周波数特性を得ることができる。複数の代表的な線数におけるFFTの結果（振幅スペクトル強度｜Ｆ（ｕ，ｖ）｜ ）を示すと、図１３〜図１５のようになる。

By using the amplitude spectrum intensity of the Fourier transform, frequency characteristics can be obtained for each mask. The FFT results (amplitude spectral intensity | F (u, v) |) for a plurality of representative lines are as shown in FIGS.

  13 to 15, the upper left figure is the original document, and the upper right, lower left, and lower right figures are for each color component, that is, R (red) plane, G (green) plane, and B (blue). The frequency characteristics (amplitude spectrum intensity) of the plane are shown.

  In the frequency characteristics of each color component, the horizontal axis is u and the vertical axis is v, and the amplitude spectrum intensity is indicated by the difference in color (see the right end of each figure). Each figure shows the frequency characteristics for the input block, u = v = 0 in the upper left indicates the DC component, and the coefficients (power) in the other u, v = 1,. The frequency from low frequency to high frequency is shown from the upper left to the lower right, and the degree to which the corresponding frequency component is included is indicated by the coefficient value.

  In the places where the amplitude spectrum intensities in each line number are concentrated, the coefficients are concentrated near the upper left for low line numbers, and near the lower right for high line numbers. This is because halftone dots with a low number of lines are constituted by halftone dots containing a lot of low frequency components, and halftone dots with a high number of lines are constituted by halftone dots containing a lot of high frequency components. Therefore, it is possible to identify the number of lines by obtaining the frequency characteristics of the area detected as a halftone dot and calculating the frequency component contained in the halftone dot.

Next, the method for determining the number of lines in step S2 will be described below. The number of lines is determined based on the distribution of frequency characteristics in the frequency range. Specifically, the amplitude spectrum intensity is determined using a value normalized by calculating the sum for each area shown in FIG. 12C and dividing by the number of pixels included in the area. At this time, for the location of the position of the peak, depending on the location used in FIG. 12 (c), the low number of lines when there is a peak in A _0, the middle line number if there is a peak in A _1, peak A ₂ is If there is, the number of high lines is determined.

In the above-described line number discrimination method, as shown in FIG. 12C, the coefficient indicating the frequency characteristic is divided, the parameter (Para0) indicating the amount including the low frequency component, and the parameter indicating the amount including the intermediate frequency component, respectively. (Para1), a parameter (Para2) indicating the amount including the high frequency component is calculated. Assuming that the locations in the frequency space used for calculating each parameter are A ₀ , A _1, and A ₂ , the parameters to be calculated are set as follows:

具体的な判定の方法は、以下の式７に示すように、求めたパラメータの内最大のパラメータ値を算出し、どの場所のパラメータが最大になるかによって線数を判定する。

As a specific determination method, as shown in the following Expression 7, the maximum parameter value among the obtained parameters is calculated, and the number of lines is determined depending on which location has the maximum parameter.

max_para = max (para0, para1, para2) (7)
In the above equation 7, when the maximum value is Para0, the number of low frequency lines is determined, when the maximum value is Para1, the number of intermediate lines is determined, and when the maximum value is para2, the number of high frequency lines is determined.

  Here, the number of low frequency lines (the number of low lines) is 65 to 85 lines, the number of intermediate lines (the number of intermediate lines) is 120 to 133 lines, and the number of high frequency lines (the number of high lines) is 150. Set to be above line. However, this setting is not limited to this. For example, as another example, the number of low frequency lines is 65 to 100 lines, the intermediate number of lines is 120 to 130 lines, and the number of high frequency lines is You may determine that it is 135 lines or more. In any case, it can be changed according to the difference in the place in the frequency space for calculating the parameter.

In addition, although the steps are divided into three steps (low frequency, intermediate, and high frequency) here, the steps can be increased depending on the mask size at the time of conversion to frequency. If the mask size for performing the frequency conversion is increased, the frequency conversion accuracy can be increased. However, since the calculation amount is increased, the conversion is performed with the above size.
The result of determination using the parameters for determining the number of lines is as shown in the following equation 8.

Jude (x, y) = {number of low lines, number of intermediate lines, number of high lines} (8)
Using the determined result, in the resolution conversion unit 51, in steps S3 to S5,
Low line count (65 to 85 lines) → Resolution 150 dpi (step S3)
Number of middle lines (120 to 133 lines) → Resolution 300 dpi (step S4)
High line number (150 lines or more) → Resolution 400 dpi (step S5)
Conversion to

  The resolution-converted image is subjected to halftone dot detection by the halftone dot determination method in step S6. In the character region determination process in step S8, determination is performed using input image data (input signal) that has not been subjected to resolution conversion.

  As described above, the resolution conversion is appropriately performed according to the type of halftone image, that is, the type of the number of halftone lines, thereby further improving the accuracy of detection of the halftone area.

  Further, the region separation method according to the present invention may be realized as software (application program). In this case, a printer driver incorporating software that realizes processing based on the result of region separation processing (region identification signal) can be provided in the computer or printer.

  As an example of the above, a process based on the region separation determination result will be described below with reference to FIG. As shown in FIG. 16, the computer 101 includes a printer driver 102, a communication port driver 107, and a communication port 108. The printer driver 102 includes a color correction unit 103, a spatial filter processing unit 104, a gradation reproduction processing unit 105, and a printer language translation unit 106.

  The computer 101 is connected to a printer (image output device) 109, and the printer 109 outputs an image in accordance with image data output from the computer 101.

  The color correction unit 103 includes the color correction unit 7 and the black generation / under color removal unit 8 shown in FIG. 1 or FIG. 7, and functions as the color correction unit 7 and the black generation / under color removal. Same as part 8.

  The spatial filter processing unit 104 and the gradation reproduction processing unit 105 correspond to the spatial filter processing unit 9 and the gradation reproduction processing unit 11 shown in FIG. 1 or 7, respectively. This is the same as the gradation reproduction processing unit 11.

  Image data generated by executing various application programs in the computer 101 is subjected to the above-described processing by the color correction unit 103, the spatial filter processing unit 104, and the gradation reproduction processing unit 105. In this case, the color correction unit 103 also includes a black generation and under color removal processing function as described above.

  A region separation signal is input to the spatial filter processing unit 104 and the gradation reproduction processing unit 105, and various processes are performed based on the region separation signal.

  The image data subjected to the above processing is converted into a printer language by the printer language translation unit 106 and input to the printer 109 via the communication port driver 107 and a communication port (for example, RS232C / LAN) 108. Note that the printer 109 may be a digital multifunction machine having a copy function and a fax function in addition to the printer function.

  The present invention also provides an image processing method for performing region separation processing on image data subjected to resolution conversion, dot number recognition, and resolution conversion on a computer-readable recording medium on which a program to be executed by a computer is recorded. It can also be recorded.

  As a result, a program for performing an image processing method that performs resolution conversion or calculates the number of halftone lines, performs resolution conversion based on the result, switches the input resolution to the halftone detection processing, and performs region separation processing The recorded recording medium can be provided in a portable manner.

  The recording medium may be a non-illustrated memory, for example, a program medium such as a ROM because processing is performed by a microcomputer, and a program reading device as an external storage device (not illustrated) is provided, and the recording medium is stored therein. It may be a program medium that can be read by being inserted.

  In any case, the stored program may be configured to be accessed and executed by the microprocessor, and the program is read out, and the read program is stored in a program storage area (not shown) of the microcomputer. A method of downloading and executing the program may be used. In this case, it is assumed that the download program is stored in the main device in advance.

  Here, the program medium is a recording medium configured to be separable from the main body, and includes a tape system such as a magnetic tape and a cassette tape, a magnetic disk such as a floppy (registered trademark) disk and a hard disk, and a CD-ROM / MO /. Disk systems for optical disks such as MD / DVD, card systems such as IC cards (including memory cards) / optical cards, mask ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash It may be a medium that carries a fixed program including a semiconductor memory such as a ROM.

  In this case, since the system configuration is capable of connecting a communication network including the Internet, the medium may be a medium that fluidly carries the program so as to download the program from the communication network. When the program is downloaded from the communication network in this way, the download program may be stored in the main device in advance or installed from another recording medium.

  The recording medium is read by a program reading device provided in a digital color image forming apparatus or a computer system, whereby the above-described image processing method is executed.

  The computer system includes an image input device such as a flatbed scanner, a film scanner, and a digital camera, a computer that performs various processes such as the image processing method by loading a predetermined program, and displays the processing results of the computer. An image display device such as a CRT display / liquid crystal display and a printer that outputs the processing results of the computer to paper. Furthermore, a network card, a modem, and the like are provided as communication means for connecting to a server or the like via a network.

  In the present embodiment, the example in which the image processing apparatus of the present invention is applied to a digital color copying machine has been described, but it may be applied to a scanner. In this case, the color image processing apparatus 1 shown in FIG. 1 may include the A / D conversion unit 2, the shading correction unit 3, the input tone correction unit 4, the resolution conversion unit 51, and the region separation processing unit 5. In the color image processing device 61 shown in FIG. 7, the A / D conversion unit 2, the shading correction unit 3, the input tone correction unit 4, the halftone line number calculation unit 71, the resolution conversion unit 51, and the region separation processing unit. It is sufficient to have up to 5.

  The region separation signal output from the region separation processing unit 5 is sent to a computer or the like and used for subsequent image processing.

  The image processing apparatus of the present invention may have the following configuration.

  That is, the image processing apparatus of the present invention converts the resolution of input image data in an image processing apparatus that includes an area separation processing unit that identifies a plurality of areas such as character areas and halftone dot areas for input image data. The image processing apparatus includes a resolution conversion unit and a control unit that changes the resolution of the input image data in accordance with a target area identified by the area separation processing unit.

  The area separation process is a process for classifying into a plurality of types having different properties using density information of local blocks. This is used to optimize the output image quality by switching the subsequent processing using the classified information.

  In order to classify regions, separation is performed based on signals input as predetermined local blocks, but the resolution suitable for detection differs depending on the type of detection, so input according to the type of detection. Different resolutions are effective in improving detection accuracy.

  The region separation processing unit includes at least a character detection unit (character determination unit) for detecting characters and a halftone detection unit (halftone determination unit) for detecting a halftone dot region. The resolution of the input image data may be different between the means and the halftone dot detection means.

  When performing the region separation process, a means for detecting normal characters and a means for detecting halftone dots are provided as separate means. However, since the input resolution suitable for each discriminating means is different, if the input resolution is different between the character detecting means and the halftone detecting means, it is possible to perform detection using the optimum input signal, and the detection accuracy. Can be increased.

  The control unit may set the resolution of the input image data input to the character detection unit to a high resolution and the resolution of the input image data input to the halftone detection unit to a low resolution.

  When detecting a character region, enhancement processing, binarization processing, or the like can be considered as subsequent processing. Therefore, the detection position accuracy as the character region must match the character position of the actual input image. Also, high resolution input is suitable for detecting fine lines in a character area. On the other hand, the detection of the halftone dot region is preferably performed by putting a wave of a signal in a wide range as much as possible into the local mask because the detection accuracy becomes higher.

  Further, a halftone dot number calculating unit for recognizing the number of halftone dot lines is provided, and the control unit switches the resolution input to the halftone dot detecting means according to the number of lines recognized by the halftone dot number calculating unit. Good.

  It is possible to switch the input resolution to the halftone detection unit as appropriate according to the number of halftone lines, and it is possible to perform halftone detection with higher accuracy using the optimum input resolution.

  The image processing method of the present invention is a resolution conversion for converting the resolution of input image data in an image processing method including an area separation process step for identifying a plurality of areas such as a character area and a halftone dot area for input image data. And a resolution conversion step of changing the resolution of the input image data in accordance with the region to be identified in the region separation processing step.

  The region separation processing unit step includes at least a character detection step for detecting characters and a halftone dot detection step for detecting a halftone dot region, and the resolution conversion step includes input image data in the character detection step and the halftone dot detection step. Different resolutions may be used.

  Further, a halftone dot number calculating step for recognizing the number of halftone dot lines may be provided, and the resolution conversion step may set the resolution according to the number of lines recognized by the halftone dot number calculating step, and may be switched depending on the area to be identified.

  The present invention is not limited to the above-described embodiments, and various modifications can be made within the scope of the claims, and the technical means disclosed in each embodiment can be appropriately combined. Such embodiments are also included in the technical scope of the present invention.

  The image processing method of the present invention can be applied to a digital color copying machine, but any other apparatus that is required to improve the reproducibility of image data that is input and output by image data. Even applicable. An example of such an apparatus is an image reading apparatus such as a scanner.

1, showing an embodiment of the present invention, is a block diagram showing a main configuration of a digital color copying machine. FIG. FIG. 2 is a block diagram showing a main configuration of a region separation processing unit of the digital color copying machine shown in FIG. 1. It is explanatory drawing which showed typically the area | region separation process in the area | region separation process part shown in FIG. It is a figure which shows the difference in the information content by resolution conversion. (A)-(c) shows the detection process process of a character area, (d)-(f) is explanatory drawing which shows the detection process of a halftone dot area | region. (A)-(c) shows the detection process process of a character area, (d)-(f) is explanatory drawing which shows the detection process of a halftone dot area | region. FIG. 10 is a block diagram illustrating a main configuration of a digital color copying machine according to another embodiment of the present invention. It is a block diagram which shows the principal part structure of the area | region separation process part shown in FIG. It is explanatory drawing which showed typically the area | region separation process in the area | region separation process part shown in FIG. (A) is a figure which shows the reference | standard original for halftone dot number detection, (b) is a table | surface which shows the halftone dot detection rate based on the relationship between a line number and resolution. It is a flowchart which shows the flow of a process in the area | region separation process part shown in FIG. (A)-(c) is a figure which shows the line number calculation process process in a halftone dot area | region. It is a figure which shows the relationship between the number of halftone lines, and a frequency. It is a figure which shows the relationship between the number of halftone lines, and a frequency. It is a figure which shows the relationship between the number of halftone lines, and a frequency. It is a figure which shows schematic structure of the computer which implement | achieves the software of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Color image processing apparatus 2 A / D conversion part 3 Shading correction part 4 Input gradation correction part 5 Area separation process part 7 Color correction part 8 Black generation under color removal part 9 Spatial filter processing part 10 Output gradation correction part 11 Floor Tone reproduction processing unit 12 Operation panel 13 Color image input device 14 Color image output device 20 Character detection unit 21 Halftone detection unit 22 Determination unit 51 Resolution conversion unit 52 Control unit 53 Memory 61 Color image processing device 71 Halftone line number calculation unit 101 Computer 102 Printer driver 103 Color correction unit 104 Spatial filter processing unit 105 Tone reproduction processing unit 106 Printer language translation unit 107 Communication port driver 108 Communication port 109 Printer

Claims (8)

An area separation processing unit that performs processing for identifying a plurality of areas such as a character area and a halftone dot area for input image data;
A resolution conversion unit that converts the resolution of the input image data input to the region separation processing unit;
A control unit that controls the resolution conversion unit so as to convert the resolution of the input image data in accordance with the target region identified by the region separation processing unit;
A halftone line number calculation unit for recognizing the number of halftone lines for the input image data ,
The control unit further controls the resolution conversion unit to switch the resolution of the input image data to be input to the region separation processing unit according to the stage in which the number of lines recognized by the halftone line number calculation unit is classified. And
An image processing apparatus, wherein the boundary value and the number of stages at which the number of lines is classified can be arbitrarily changed. The region separation processing unit includes at least character detection means for detecting a character and halftone dot detection means for detecting a halftone dot region,
The control unit controls the resolution conversion unit so that a resolution of input image data input to the character detection unit is different from a resolution of input image data input to the halftone detection unit. The image processing apparatus according to claim 1.   The control unit controls the resolution conversion unit so that the resolution of the input image data input to the halftone dot detection unit is lower than the resolution of the input image data input to the character detection unit. The image processing apparatus according to claim 2.   An image forming apparatus comprising the image processing apparatus according to claim 1. In an image processing method including an area separation process step for identifying a plurality of areas such as a character area and a halftone dot area for input image data,
In the region separation processing step, a resolution conversion step of changing the resolution of the input image data according to the region to be identified,
A halftone dot number calculating step for recognizing the number of halftone dots for the input image data ,
In the resolution conversion step, the resolution is further switched according to the stage where the number of lines recognized by the halftone line number calculation step is classified.
An image processing method characterized in that the boundary value and the number of stages at which the number of lines is classified can be arbitrarily changed. The region separation processing step includes a character detection step for detecting at least a character, and a halftone dot detection step for detecting a halftone dot region,
6. The image processing method according to claim 5, wherein in the resolution conversion step, the resolution of the input image data is made different between the character detection step and the halftone dot detection step. A computer program for causing a computer to execute an area separation process for identifying a plurality of areas such as a character area and a halftone dot area,
A computer program for causing the computer to function as each unit of the image processing apparatus according to claim 1.   A computer-readable recording medium, wherein the program according to claim 7 is stored in a computer so as to be executable.

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