JP4903254B2 - Image processing apparatus, image forming apparatus, image processing method, computer program, and recording medium - Google Patents

Description

  The present invention relates to an image processing apparatus capable of preventing image quality deterioration by filtering, an image forming apparatus including the image processing apparatus, an image processing method, a computer program for realizing the image processing apparatus, and the computer program The present invention relates to a recording medium that can be read by a computer on which the information is recorded.

  Until the image is input by an image input device such as a scanner or a digital camera and the image is output by an image output device such as a display or a printer, some image processing is combined with the input image data. Applied. As an image sensor of an optical input device such as a scanner or a digital camera, a CCD (Charge Coupled Device) image sensor, a CMOS (Complementary Metal Oxide Semiconductor) image sensor, a CIS (Contact Image), or the like. Sensor: contact type image sensor) or the like is used. In a scanner or a digital camera, the light collected through the lens is converted into an electrical signal by photoelectric conversion using an image sensor, and the electrical signal is A / D converted to obtain a captured image as digital data. The digital data is subjected to image processing, converted into a form suitable for the image output device, and output.

  For example, a scanner using a one-dimensional line CCD sensor as an image pickup device as an image input device and a device called a multi-function printer or a digital copying machine having an ink jet type or laser type printer as an image output device are taken as examples. As described in Patent Document 1, several image processes are performed until a captured image of a document read by a scanner is output to a printer. Generally, when image processing is performed using an input captured image, for example, visibility correction processing and filter processing are performed.

  The visibility correction process is performed in order to match the digital data value of the captured image with human visual characteristics. The conversion characteristics of the image that has passed through the image sensor and the A / D conversion process are usually linear characteristics, and the digital data value increases in proportion to the increase in the amount of light to the image sensor. On the other hand, it is known that human visual characteristics have logarithmic characteristics with respect to the amount of incident light. For this reason, the visibility correction processing is performed for the purpose of facilitating subsequent image processing by converting the digital data value after A / D conversion into a digital data value that matches human visual characteristics.

  The filtering process is performed for various purposes. For example, the purpose is to improve the deterioration of the spatial frequency characteristics of the captured image due to the MTF (Modulation Transfer Function) characteristics of the optical lens used in the optical input device. The digital data after performing visibility correction on the image is used as the input image. For example, Patent Document 2 discloses spatial filtering processing that mixes an edge enhancement processing result and a smoothing result for an image from an edge determination result. Patent Document 3 discloses an image filter that mixes a filter calculation result for an image and an original image at a mixing ratio calculated from contrast. Patent Document 4 discloses an image filter that switches the content of smoothing processing using a pixel value difference between a target pixel and surrounding pixels.

Japanese Patent No. 3472479 JP 61-157169 A JP-A-10-271340 JP-A-7-288768

  The digital data of the captured image output from the optical input device includes noise. This noise is caused by the image sensor and that caused by the A / D conversion process. The noise caused by the image sensor is roughly classified into noise depending on the output electric signal (light quantity dependent noise) and output. It can be divided into noise that does not depend on electrical signals (noise independent of light amount). Also, noise caused by A / D conversion processing does not depend much on the digital output value.

  The light amount-dependent noise is caused by variations in the number of electrons subjected to photoelectric conversion, and is proportional to the half power of the light amount. The light amount-independent noise and the noise caused by the A / D conversion process are caused by random noise of the amplifier circuit, variation between elements of the imaging device, conversion error of the A / D converter, or the like. In general, since there are limits to measures for reducing random noise from the viewpoint of cost, the amount of random noise that does not depend on the digital value is dominant as the total amount of noise. As a result, the same amount of noise is superimposed on the entire data value in the digital data of the captured image.

  As described above, the visibility correction processing performs nonlinear conversion on the digital data value of the captured image. FIG. 31 is an explanatory diagram showing an example of the relationship between the input value and the output value of the conventional visibility correction process. In FIG. 31, the horizontal axis represents an input value, and the vertical axis represents an output value. 10-bit R (Red), G (Green), and B (Blue) digital data (input values) represented by values from 0 to 1023 are represented by R, G, B, converted into 8-bit values and output. As shown in FIG. 31, in the region where the RGB digital data value is low, that is, in the high density portion (dark portion), the input value of the region where the RGB digital data value is high, ie, in the low density portion (bright portion). The change of the output value with respect to the change is large.

  FIG. 32 is an explanatory diagram showing the change amount of the output value with respect to the input value in the conventional visibility correction processing. FIG. 32 is a change amount of the output value in the example of FIG. 31. When the input value changes from 100 to 120 (input value change amount is 20) and when the input value changes from 900 to 920 (input value). The amount of change indicates the amount of change in the output value in 20). The parentheses indicate the amplification factor (gain) with respect to the slope 0.25 (= 256/1024) when the input and the output are completely proportional (linear). As shown in FIG. 32, both of the input values have changed by 20, whereas when the change from 100 to 120, which is the high density portion, the output value is amplified in the range of 3.4 times to 4.0 times. In the change from 900 to 920, which is the low density portion, the output value indicates that attenuation in the range of 0.2 to 0.4 times is performed. As a result, in the output of the visibility correction process, the noise in the high density part is amplified and the noise in the low density part is attenuated.

  For this reason, in the filtering process after the visibility correction process, the digital data in a state where the noise in the high density part is large and the noise in the low density part is not conspicuous is input as an input image. In the filter process, a filter process having an emphasis characteristic is performed to improve the MTF characteristic, but the filter process of the emphasis characteristic further amplifies noise amplified near a high density.

  FIG. 33 is an explanatory diagram showing an example of image data before and after the filter processing having the emphasis characteristic. In FIG. 33, the horizontal axis indicates the pixel position, and the vertical axis indicates the data value (density value) of the image data. FIG. 33 shows an example of the result after applying filter processing having enhancement characteristics to the input image data to the filter processing for transition from low density (mainly the background area) to high density (mainly the solid area). . As shown in FIG. 33, the input image data in the low density region is less affected by noise and the input value variation is less, whereas the high density region (solid region) has larger input value variation due to the noise. As a result of performing the filter processing having the emphasis characteristic, noise in the high density region is further amplified, and there is a problem that the variation in the data value after the filter processing becomes large.

  On the other hand, since the digital data values before and after the filtering process are composed of 8-bit integers from 0 to 255, the filtering process result below 0 is clipped to 0 as the calculation result. Since the negative part of the noise wave height is limited, the average value of the digital data value before the filtering process and the digital data value after the filtering process at the pixel position (pixel area) indicated by C in FIG. 33 was calculated. In this case, for example, the average value before the filter process is 5.3, whereas the average value after the filter process is 9.0, and the average density is increased by the filter process.

  The influence on the image quality generated by the above-described problem is particularly remarkable in a solid portion where the input value is constant in the high density region of the image, and light and shade are generated in the high density region of the image, and a rough feeling is generated in the image. . In addition, since the density value after the filtering process is increased, the density of the image is reduced (becomes brighter) and the contrast is lowered. In order to eliminate this, it is effective to perform a filter process having a smoothing characteristic instead of an enhancement characteristic. However, on the other hand, when a filtering process having smooth characteristics is performed, sharpness is lost particularly at the edge portion, and thus an adverse effect on image quality occurs. Therefore, in the conventional technique, focusing on the preservation of the edge portion, the edge portion or a portion having a large contrast difference is detected, and the smoothness is suppressed in the edge portion or the portion having a large contrast difference, thereby aiming at avoidance. However, when an edge is stored using an edge portion or contrast difference, an edge can be stored for a light character, but a high-density region (mainly a solid region) having a density difference comparable to that of a light character. ), Noise could not be suppressed, and shading occurred, and the above two problems could not be solved.

  The present invention has been made in view of such circumstances, and an image processing apparatus capable of preventing image quality deterioration by filtering, an image forming apparatus provided with the image processing apparatus, an image processing method, and the image processing It is an object of the present invention to provide a computer program for realizing the apparatus and a computer-readable recording medium on which the computer program is recorded.

An image processing apparatus according to the present invention calculates density of a pixel area based on a pixel value of a pixel area constituted by a plurality of pixels of an input image in an image processing apparatus that performs image processing on input image data. A density calculating unit that performs the smoothing process on the input image data when the density calculated by the density calculating unit is greater than a predetermined first density threshold, and the density calculated by the density calculating unit. Is smaller than a predetermined second density threshold value (<the first density threshold value) , the input image data is subjected to enhancement processing, mixing processing of enhancement processing and smoothing processing, or strong smoothing processing and weak smoothing processing. The second processing means for performing any one of the processes in combination with the image data processed by each of the first processing means and the second processing means according to the density calculated by the density calculation means. Characterized in that it comprises a weighting processing unit for the assigned process is performed.

An image processing apparatus according to the present invention includes a storage unit that stores a first filter coefficient for performing a smoothing process on input image data in an image processing apparatus that performs digital filter arithmetic processing on input image data; Stores a second filter coefficient for performing any of enhancement processing, mixing processing of enhancement processing and smoothing processing, or processing combining strong smoothing processing and weak smoothing processing on input image data. Storage means for calculating the density of the pixel area based on the pixel value of the pixel area constituted by a plurality of pixels of the input image, and according to the density calculated by the density calculation means, a first coefficient for weighting the first filter coefficients, and the weighting coefficient setting means for setting a second coefficient for weighting relative to the second filter coefficients, prior Kiomomi coefficient Using the first coefficient set by the determining means, the first filter coefficient is weighted, the second filter coefficient is weighted using the second coefficient, and each weighted filter coefficient is added. and weighting processing means for generating a new filter coefficients, the input image data, characterized by comprising a calculating means for performing a filter operation using the new filter coefficients generated by the weighting processing means.

  In the image processing apparatus according to the present invention, the density calculation unit is configured to calculate a density based on pixel values of a pixel area of each of a plurality of specific colors, and the weighting processing unit includes the density calculation According to the density calculated by the means, a common weighting process is applied to each of the specific colors.

  An image forming apparatus according to the present invention includes the image processing apparatus according to any one of the above-described inventions, and an image forming unit that forms an image processed by the image processing apparatus.

The image processing method according to the present invention is an image processing method for performing image processing on input image data, and calculates a density of the pixel region based on a pixel value of a pixel region constituted by a plurality of pixels of the input image. A first processing step for performing a smoothing process on the input image data when the density calculated in the step for calculating the density is greater than a predetermined first density threshold, and a step for calculating the density. When the calculated density is smaller than a predetermined second density threshold value (<the first density threshold value) , the input image data is emphasized, mixed with the emphasizing process and the smoothing process, or strongly smoothed and weak. The first processing step and the second processing step are performed in accordance with the second processing step for performing any one of the processing combined with the smoothing processing and the density calculated in the step for calculating the density. Characterized in that it comprises the step of applying a weighting process to the processed image data in step respectively.

  An image processing method according to the present invention stores a first filter coefficient for performing smoothing processing on input image data in an image processing method for performing digital filter arithmetic processing on input image data. Stores a second filter coefficient for performing any one of enhancement processing, mixed processing of enhancement processing and smoothing processing, or processing combining strong smoothing processing and weak smoothing processing on image data. In accordance with the density calculated in the step of calculating the density of the pixel area based on the pixel value of the pixel area constituted by a plurality of pixels of the input image and the step of calculating the density, A step of setting a first coefficient for weighting one filter coefficient and a second coefficient for weighting the second filter coefficient, and setting in the setting step The first filter coefficient is weighted by using the first coefficient, the second filter coefficient is weighted by using the second coefficient, and the weighted filter coefficients are added and newly added. And a step of performing a filter operation on input image data using the filter coefficient created in the creating step.

A computer program according to the present invention is a computer program for causing a computer to perform image processing on input image data, wherein the computer is based on a pixel value of a pixel region configured by a plurality of pixels of the input image. A density calculating means for calculating the density of the pixel region; a first processing means for performing a smoothing process on input image data when the density calculated by the density calculating means is greater than a predetermined first density threshold; and When the density calculated by the density calculation means is smaller than a predetermined second density threshold (<the first density threshold) , the input image data is enhanced, mixed with enhancement and smoothing, or strongly smoothed. A second processing unit that performs any one of a combination of processing and a weak smoothing process, and the first calculation unit according to the density calculated by the density calculation unit. And wherein the function as weighting processing means for performing a weighting processing for the image data processed by each management unit and the second processing means.

A computer program according to the present invention is a computer program that causes a computer to perform digital filter arithmetic processing on input image data. The computer is based on pixel values of a pixel area formed by a plurality of pixels of an input image. A density calculating means for calculating the density of the pixel area; and a first weighting unit for weighting a first filter coefficient for performing a smoothing process on the input image data according to the density calculated by the density calculating means. A second process for performing an enhancement process on the input image data, a mixed process of the enhancement process and the smoothing process, or a process combining a strong smoothing process and a weak smoothing process. a weighting factor setting means for setting a second coefficient for weighting the filter coefficient, the first coefficient set in the previous Kiomomi coefficient setting means Weighting used, performs weighting on the first filter coefficients, performs weighting on the second filter coefficients using the second coefficient, generates a new filter coefficient by adding a filter coefficient performed respectively weighting and processing means, the input image data, characterized in that to function as a calculating means for performing a filter operation using the new filter coefficients generated by the weighting processing means.

  A recording medium readable by a computer according to the present invention is recorded with a computer program according to any one of the aforementioned inventions.

  In the present invention, the density of the pixel area is calculated based on the pixel value of the pixel area constituted by a plurality of pixels of the input image. The pixel area can be, for example, an area of 5 pixels × 5 pixels with the attention point at the center. Moreover, the density | concentration to calculate can be made into the average value of a density | concentration value, for example. When the calculated density is larger than a predetermined first density threshold (the first density threshold is a threshold for determining whether or not the density is high, and when the pixel area is a high density area, for example, a solid area or the like The dark portion) and the input image data are subjected to a smoothing process (first process). Further, when the density of the pixel area is smaller than a predetermined second density threshold (the second density threshold is a threshold for determining whether or not the density is low, and the pixel area is a low density area), the input image A process (second process) different from the smoothing process performed when the pixel area is a high density area is applied to the data. In this case, the processing (second processing) different from the smoothing processing (first processing) is, for example, enhancement processing, mixing processing of enhancement processing and smoothing processing, or a combination of strong smoothing processing and weak smoothing processing. But you can.

  The pixel region density for each of the image data obtained by smoothing the input image data and the image data obtained by subjecting the input image data to a second process different from the smoothing process. The weighting process is performed according to the above, and each weighted image data is added. For example, assuming that the weighting coefficient for the image data after the smoothing process is α and the weighting coefficient for the image data after the second processing is β, when the density of the pixel region is high (dark part), α = 1, β = If 0 and the density of the pixel region is low (bright), α = 0 and β = 1, and α is increased from 0 to 1 as the density of the pixel region changes from low to high. At the same time, β can be reduced from 1 to 0. Thereby, while suppressing the noise in a high density area | region, the increase in the average density after the filter process resulting from noise can be suppressed, and image quality degradation can be prevented. Further, edge deterioration can be prevented in a low density region (for example, a bright portion such as a base region). Furthermore, even if the density of the input image is changed by continuously changing the weighting coefficients (mixing ratios) α and β as the density of the pixel region transitions from the low density side to the high density side, the output image It is possible to suppress an abrupt change in the density of the image and to further prevent the deterioration of the image quality.

  In the present invention, the density of the pixel area is calculated based on the pixel value of the pixel area constituted by a plurality of pixels of the input image. The pixel area can be, for example, an area of 5 pixels × 5 pixels with the attention point at the center. Moreover, the density | concentration to calculate can be made into the average value of a density | concentration value, for example. Each of the first filter coefficient and the second filter coefficient is subjected to weighting processing according to the density of the pixel region, and the filter means is used to perform filter calculation using the filter coefficient obtained by adding the weighted filter coefficients. . Here, the first filter coefficient is, for example, a filter coefficient for performing a smoothing process on the input image data. In addition, the second filter coefficient is, for example, an enhancement processing operation on input image data, a mixed processing operation of the enhancement processing operation and the smoothing processing operation, or a combination of a strong smoothing operation and a weak smoothing operation. It is a filter coefficient for performing.

  When the weighting coefficient for the first filter coefficient is α (first coefficient) and the weighting coefficient for the second filter coefficient is β (second coefficient), for example, when the density of the pixel region is high (dark part), When α = 1 and β = 0 and the density of the pixel region is low (bright portion), α = 0 and β = 1, and α is set in accordance with the density of the pixel region from low to high. While increasing from 0 to 1, β can be decreased from 1 to 0. That is, when the pixel area is a high density area (for example, a dark area such as a solid area), the calculation means performs the filter calculation using the first filter coefficient for performing the smoothing process on the input image data. become. Thereby, while suppressing the noise in a high density area | region, the increase in the average density after the filter process resulting from noise can be suppressed, and image quality degradation can be prevented.

  When the pixel region is a low density region, the calculation means performs a filter calculation using the second filter coefficient on the input image data. Thereby, in a low density region (for example, a bright portion such as a base region), an edge can be preserved to prevent the deterioration of the edge. Furthermore, even if the density of the input image is changed by continuously changing the weighting coefficients (mixing ratios) α and β as the density of the pixel region transitions from the low density side to the high density side, the output image It is possible to suppress an abrupt change in the density of the image and to further prevent the deterioration of the image quality.

  Further, since the calculation means is shared and the filter coefficient used for the filter calculation is selected according to the density of the pixel area, for example, a plurality of calculation processing units are provided according to the density of the pixel area, The amount of processing can be reduced compared with the case where processing is performed by the processing unit. In particular, when the filter size (filter matrix size) is large, the amount of processing can be further reduced. In addition, when the number of filter coefficient types is small, the processing amount can be optimized.

  In the present invention, the density of the pixel region is determined based on the pixel values of the pixel regions of a plurality of specific colors, for example, R (red), G (green), and B (blue). In accordance with the density, a common weighting process is performed for each specific color. For example, the same weighting coefficients (mixing ratios) α and β are used for each specific color. By using a common weighting coefficient, it is possible to perform a common filter process or filter operation for all colors, and to prevent non-uniformity in the process or calculation at the time of mixed color and single color.

  In the present invention, it is possible to form an image in which deterioration of image quality is prevented by forming an image processed by the image processing apparatus.

  According to the present invention, it is possible to suppress noise in a high density region, suppress an increase in average density after filtering due to noise, and prevent image quality deterioration. Further, edge deterioration can be prevented in a low density region (for example, a bright portion such as a base region). Furthermore, even when the density of the input image is changed, it is possible to suppress a sudden change in the density of the output image, and it is possible to further prevent deterioration of the image quality.

1 is a block diagram illustrating a configuration of an image forming apparatus including an image processing apparatus according to the present invention. It is a block diagram which shows an example of a structure of a spatial filter process part. It is explanatory drawing which shows an example of local density | concentration calculation of a pixel area. It is explanatory drawing which shows an example of the reference pixel area | region for filter calculation. It is explanatory drawing which shows the structural example of a spatial filter kernel. It is explanatory drawing which shows an example of the filter coefficient used by a 1st filter calculating part. It is explanatory drawing which shows an example of the filter coefficient used in a 2nd filter calculating part. It is explanatory drawing which shows an example of a weighting coefficient. It is a flowchart which shows an example of the procedure of the spatial filter process of an image process part. FIG. 4 is a block diagram illustrating a configuration of an image forming apparatus including an image processing apparatus according to a second embodiment. It is a block diagram which shows the structure of an area | region separation process part. 6 is a block diagram illustrating an example of a configuration of a spatial filter processing unit according to Embodiment 2. FIG. It is explanatory drawing which shows an example of the filter coefficient which has an emphasis characteristic. It is explanatory drawing which shows an example of the setting of a weighting coefficient. 10 is a flowchart illustrating an example of a procedure of spatial filter processing of the image processing unit according to the second embodiment. 10 is a block diagram illustrating an example of a configuration of a spatial filter processing unit according to Embodiment 3. FIG. It is explanatory drawing which shows the structural example of a spatial filter kernel. 12 is a flowchart illustrating an example of a procedure of spatial filter processing of the image processing unit according to the third embodiment. FIG. 10 is a block diagram illustrating an example of a configuration of a spatial filter processing unit according to a fourth embodiment. 15 is a flowchart illustrating an example of a procedure of spatial filter processing of the image processing unit according to the fourth embodiment. It is explanatory drawing which shows the other example of a weighting coefficient. It is explanatory drawing which shows an example of the filter coefficient which has a smoothing characteristic. FIG. 20 is a block diagram illustrating an example of a configuration of a spatial filter processing unit according to a sixth embodiment. It is a block diagram which shows an example of a structure of each color space filter part. FIG. 20 is a block diagram illustrating an example of a configuration of a spatial filter processing unit according to a seventh embodiment. It is explanatory drawing which shows an example of the selection method of a filter coefficient. It is explanatory drawing which shows an example of a 1st filter coefficient. It is explanatory drawing which shows an example of a 2nd filter coefficient. It is explanatory drawing which shows an example of a 3rd filter coefficient. 27 is a flowchart illustrating an example of a procedure of spatial filter processing of the image processing unit according to the seventh embodiment. It is explanatory drawing which shows an example of the relationship between the input value and output value of the conventional visibility correction process. It is explanatory drawing which shows the variation | change_quantity of the output value with respect to the input value in the conventional visibility correction process. It is explanatory drawing which shows an example of the image data before and after the filter process which has an emphasis characteristic.

Embodiment 1
Hereinafter, the present invention will be described with reference to the drawings illustrating embodiments. FIG. 1 is a block diagram illustrating a configuration of an image forming apparatus including an image processing apparatus according to the present invention. An image forming apparatus (for example, a digital color copying machine) includes an image processing unit 100 as an image processing apparatus, an image input unit 110, an image output unit 120, an operation panel 130, and the like.

  The image input unit 110 includes a light source for irradiating reading light to the original, a CCD line sensor (not shown), and the light reflected from the original is R (red) and G (green). ) And B (blue), the color signal is converted into an electrical signal to obtain a color image signal (RGB reflectance signal). Since the line sensor is an imaging device, the light source and the CCD line sensor are scanned perpendicularly (sub-scanning direction) to the long side (main scanning direction) of the line sensor to read a two-dimensional image. The generated electric signal is subjected to A / D conversion processing to be converted into digital data, and is output to the subsequent image processing unit 100 as image data.

  The image processing unit 100 outputs each output image to the image output unit 120 after performing each process described later on the input image data.

  The image output unit 120 includes an electrophotographic printing unit or a printing unit such as an inkjet method. The printing unit forms an image on a sheet such as paper or an OHP film, and outputs the image.

  The operation panel 130 includes a display device such as a setting button for setting the operation mode of the digital copying machine, a numeric keypad, and a liquid crystal display.

  The image processing unit 100 includes an A / D conversion unit 10, a shading correction unit 20, a visibility correction unit 30, a spatial filter processing unit 50, a scaling processing unit 60, a color correction unit 70, a halftone output tone processing unit 80, and the like. It has.

  The shading correction unit 20 performs a shading correction process on the reflectance signal that has been A / D converted by the A / D conversion unit 10. The shading correction process is performed to remove various distortions that occur in the image signal due to the configuration of the illumination system, the imaging system, and the imaging system of the image input unit 110.

  The visibility correction unit 30 is a LUT (Look Up) provided separately for each of the R, G, and B signals in order to correct the difference between the sensitivity characteristics of the CCD line sensor that is an image sensor and the human visibility characteristics. Table) A data value corresponding to the input value of the memory is read and processed as an output value. In the LUT memory, for example, data having conversion characteristics as shown in FIG. 31 is stored.

  The spatial filter processing unit 50 performs spatial filter processing on the input image data. Details will be described later.

  The scaling processing unit 60 absorbs the difference in resolution between the image input unit 110 and the image output unit 120, and converts the image data input with the resolution and the image size depending on the image input unit 110 into the image output unit 120. The image resolution and image size are changed according to the above.

  The color correction unit 70 converts R, G, and B signals into density signals of C (cyan), M (magenta), Ye (yellow), and K (black), and faithfully reproduces colors in the image output unit 120. In order to realize this, color correction processing is performed on density signals of C, M, Ye, and K. Specifically, the color correction process is a process of removing color turbidity based on the spectral characteristics of C, M, Ye, and K color materials each including an unnecessary absorption component from the density signals of C, M, Ye, and K. .

  The halftone output tone processing unit 80 performs tone correction processing and halftone generation processing on the C, M, Ye, and K image data. The halftone generation process is a process for dividing an image into a plurality of pixels so that gradation can be reproduced. A binary or multi-value dither method, an error diffusion method, or the like can be used. The halftone output gradation processing unit 80 can also perform processing for converting the density value of the image data into a halftone dot area ratio that is a characteristic value of the image output unit 120. The density signal processed by the halftone output tone processing unit 80 is output to the image output unit 120.

  The operation of the image processing unit 100 is controlled by a CPU (Central Processing Unit) (not shown), for example. The image forming apparatus is not limited to a digital copying machine, and may be, for example, a digital color multifunction machine having a copier function, a printer function, a facsimile transmission function, a scan to e-mail function, and the like. The digital color multi-function peripheral further includes a communication device including a modem or a network card, for example. When transmitting a facsimile, when a modem is used to perform a transmission procedure with the other party and the transmission is ready, image data compressed in a predetermined format (image data read by the scanner) is read from the memory. It performs necessary processing such as reading and changing the compression format, and sequentially transmits to the other party via a communication line. When receiving a facsimile, the CPU receives image data transmitted from the other party while performing a communication procedure and inputs the received image data to the image processing unit 100. The image processing unit 100 compresses and acquires the acquired image data. Extension processing is performed by an extension processing unit (not shown). The decompressed image data is subjected to rotation processing or resolution conversion processing as necessary, and is subjected to output tone correction processing, tone reproduction processing, and the like, and then output to the image output unit 120. In addition, data communication can be performed with a computer or other digital multi-function peripheral connected to the network via a network card or a LAN cable. Note that the present invention is not limited to a color multifunction peripheral, and may be a monochrome multifunction peripheral.

  FIG. 2 is a block diagram illustrating an example of the configuration of the spatial filter processing unit 50. The spatial filter processing unit 50 includes a local concentration calculation unit 51, a mixture ratio calculation unit 52, a first filter calculation unit 53, a second filter calculation unit 54, a weighting processing unit 55, and the like. The weighting processing unit 55 includes a multiplier 551. , 552 and an adder 553.

  The spatial filter processing unit 50 uses, as input image data, image data in which 8-bit data of R, G, and B is 1-pixel data, and outputs 8-bit data of R, G, and B after a predetermined filter processing calculation. Output image data as data of one pixel. The spatial filter processing unit 50 performs filter processing calculation independently for each of R, G, and B, and does not refer to other color data in the calculation for each color. Hereinafter, the processing content of a single plane color (any of R, G, and B) is shown. The processing contents are the same for other colors.

  The local density calculation unit 51 calculates the density of the pixel region based on the pixel value in the pixel region having a required size centered on the target point (target pixel) of the input image.

  FIG. 3 is an explanatory diagram illustrating an example of calculating the local density of the pixel region. As shown in FIG. 3, the pixel area has (2H + 1) pixels in the main scanning direction of the input image, that is, the pixel line direction of the CCD line sensor, and (2V + 1) in the sub-scanning direction of the image, that is, the scanning direction of the CCD line sensor. ) Pixels, for example, (2H + 1) = 5 pixels and (2V + 1) = 5 pixels.

  The local density calculation unit 51 multiplies each pixel value of 25 pixels including the target point by 1, and divides the multiplication result by the size of the pixel region (25 pixel number). Thereby, the local density calculation unit 51 calculates an average density value in the pixel region. When the pixel value of the input image is 0 to 255 (8 bits), the local density calculation unit 51 subtracts the average density value calculated from 255 to obtain the local density value (density) of the final pixel area. Calculate as

  Since the calculation of the spatial filter processing unit 50 uses R, G, and B signals, the closer the density value is to 0, the higher the density side, and the closer the density value is to 255, the lower the density side. Since the density value calculated from 255 is subtracted from the local density value, the lower the local density value is, the lower the density (bright part), and the closer the local density value to 255, the higher density (dark part).

  When C, M, Y, or C, M, Y, K signals are used as image data, the lower the density value is, the lower the density side is, and the closer the density value is 255, the higher the density side. . Since the density value calculated from 255 is subtracted from the local density value, the lower the local density value is to 255, the lower the density (bright part), and the closer the local density value to 0, the higher the density (dark part).

  The calculation of the local density value is not limited to the configuration in which the calculation is based on the average density of the pixel area. The local density is a density that represents the target point (target pixel) and the influence of noise included in the input image. It only needs to be able to find it. For example, among the reference pixels (pixels in the pixel area), the density average value of a plurality of pixels having a median value (or a pixel having a specific number of high density values and pixels having a specific number of low density values) (or It is also possible to use a single pixel density value) as a local density value. Alternatively, a local density value can be obtained by using a density average value using only pixels having a high density value, excluding only a specific number of pixels having a low density value from among the referenced pixels. Alternatively, in the example of FIG. 3, all the weights of each pixel in the pixel area are the same, but a weighted density average value can be used in which the weight of the attention point is increased and the weight is decreased as the distance from the attention point is increased. .

  Further, the reference position or the number of reference pixels of the peripheral pixels including the attention point is not limited to this. For example, when the resolutions of the input image in the main scanning direction and the sub scanning direction are different, the number of reference pixels in the main scanning direction and the sub scanning direction can be changed. In addition, for example, the reference position is based on the idea of a constant inter-pixel distance from the attention point. For example, in addition to referring to a square (or rectangular) shape centered on the attention point as a reference pixel, a circular reference range (pixel Area). In order to reduce the calculation amount to be calculated, for example, only a plurality of pixels on the same line including the attention point and a plurality of pixels on the same column including the attention point can be used as the reference pixels.

  The first filter calculation unit 53 performs a filter calculation on the input image data using a predetermined filter coefficient for filter calculation.

  FIG. 4 is an explanatory diagram showing an example of a reference pixel region for filter calculation. As shown in FIG. 4, an area of (2H + 1) pixels in the main scanning direction of the input image, ie, the pixel line direction of the CCD line sensor, and (2V + 1) pixels in the sub-scanning direction of the image, ie, the scanning direction of the CCD line sensor (see Pixels in the pixel area) are referred to as reference pixels, and calculation is performed using the reference pixels and a spatial filter kernel described later. The calculation result is output as a result for the target point (x, y) (target pixel) which is the center position of the reference pixel region.

  FIG. 5 is an explanatory diagram showing a configuration example of the spatial filter kernel. As shown in FIG. 5, the spatial filter kernel has a size corresponding to the reference pixel region, and filter coefficients K (−H, −V),..., K (0, 0),. K (H, V). The first filter calculation unit 53 performs a convolution calculation according to Equation (1).

  Here, O (x, y) indicates the filter calculation result for the target point (x, y), and I (i, j) is the coordinates of the reference pixel.

  The first filter calculation unit 53 performs a smoothing process on the input image data by using the filter coefficient K for the filter calculation described above.

  FIG. 6 is an explanatory diagram showing an example of filter coefficients used in the first filter calculation unit 53. As shown in FIG. 6, the size of the spatial filter kernel (filter coefficient) has 49 filter coefficients K corresponding to a reference pixel area of 7 pixels × 7 pixels. For each reference pixel, a convolution operation is performed using a coefficient set having a positive coefficient. As a result, it is possible to obtain a filter processing result having smoothing characteristics using reference pixels over a wide area of 7 × 7 pixels.

  The second filter calculation unit 54 has the same configuration as the first filter calculation unit 53. However, the filter coefficient K for filter calculation used in the second filter calculation unit 54 is different from the filter coefficient K used in the first filter calculation unit 53.

  FIG. 7 is an explanatory diagram showing an example of filter coefficients used in the second filter calculation unit 54. As shown in FIG. 7, the size of the spatial filter kernel has 49 filter coefficients K corresponding to a reference pixel area of 7 pixels × 7 pixels. For each reference pixel, a convolution operation is performed using a coefficient set having mixed positive and negative coefficients. Thereby, it is possible to obtain a mixed characteristic filtering process result having both the emphasis characteristic and the smoothing characteristic. Note that examples of the mixing characteristics are not limited to this, but only enhancement processing or a combination of strong smoothing processing and weak smoothing processing may be used.

  The mixing ratio calculation unit 52 generates weighting coefficients (mixing ratios) α and β according to the local concentration values calculated by the local concentration calculation unit 51, and outputs the generated weighting coefficients α and β to the weighting processing unit 55. .

  The weighting processing unit 55 multiplies the result calculated by the first filter calculating unit 53 by the weighting coefficient α by the multiplier 551 and the weighting coefficient β by the multiplier 552 for the result calculated by the second filter calculating unit 54. Are multiplied by the multipliers 551 and 552, added by the adder 553, and output as a processing result of the spatial filter processing unit 50.

  FIG. 8 is an explanatory diagram showing an example of the weighting coefficients α and β. In FIG. 8, the horizontal axis represents the local density value calculated by the local density calculation unit 51, and the vertical axis represents the weighting value. As shown in FIG. 8, in an area where the local density value is small (low density area), the weighting coefficient α is set to 0 and the weighting coefficient β is set to 1. In this case, the spatial filter processing result is the same as the output of the second filter calculation unit 54, that is, the mixed characteristic filtering process result having both the emphasis characteristic and the smoothing characteristic shown in the example of FIG.

  In the region where the local density value is large (high density region), the weighting coefficient α is set to 1 and the weighting coefficient β is set to 0. In this case, the spatial filter processing result is the same as the output of the first filter arithmetic unit 53, that is, the smoothing filter processing result shown in the example of FIG.

  In the region where the local density value transitions from the low density side to the high density side, the weighting coefficients α and β can be continuously changed so that, for example, α + β = 1.

  As shown in FIG. 8, the weighting coefficient α is increased from 0 when the local density value is D1. As this local density value D1, for example, a higher density side than an output value at which an amplification factor (gain) of an output value with respect to an input value in the visibility correction unit 30 is 1 or more can be used as a guide. For example, in the case of the characteristics shown in the example of FIG. 31, if the output value at which the amplification factor is 1 or more is approximately 150, by setting the value of D1 to 105 (255-150), for example, the visibility correction unit The noise that has been amplified and mixed at 30 can be canceled out by smoothing. On the other hand, the smoothing may cause an adverse effect that the boundary (character edge portion) between the character and the character background becomes dull and the character becomes unclear. Therefore, if this point is taken into consideration, the weighting coefficient α is set to 0. It is preferable that the point to be increased is closer to the high concentration side. Therefore, the local density value D1 can be set between 105 and 255 in order to suppress both image quality degradation due to noise and dullness of the character edge due to smoothing.

  On the other hand, the weighting coefficient α is set to 1 when the local density value is D2. As the local density value D2, for example, a high density value that does not occur in the character edge portion, for example, a value such as 250 can be set. This is because the character edge portion is an area where the background density and the density of the image area exist, and therefore, a high density value such as a local density value of 250 cannot be obtained.

  When the local density value is between D1 and D2, the weighting coefficients α and β are changed so as to satisfy the relationship of α + β = 1. As a result, even when the input value changes continuously, it is possible to prevent a sudden change in the output value, and to suppress noise in the high concentration range by applying a filter operation having smoothing characteristics. However, it is possible to preserve the edges and obtain a good image processing result by performing the filter operation of the mixing characteristic in the density range other than the high density range.

  The smoothing characteristic for suppressing the noise amplified in the high density region (dark part) by the calculation performed by the first filter calculation unit 53 using the filter coefficient as in the example of FIG. 6 is limited to the above example. is not. For example, when the pixels in the image are expressed as frequency components as two-dimensional basic periods with the main scanning direction and the sub-scanning direction as orthogonal axes, the frequency characteristics of noise mixed from the outside are lower (low) Therefore, various filter coefficients can be used as long as they have filter characteristics for attenuating the frequency band of these noises.

  Further, in the above-described example, filter processing using FIR (Finite Impulse Response) type convolution calculation using a total of 49 pixels of 7 pixels in the main scanning direction and 7 pixels in the sub-scanning direction as the filter calculation. Although it is exemplified in the method, the required smoothing characteristic, or the emphasis characteristic that amplifies only a specific frequency range, or the specific frequency range has attenuation, that is, the smoothing characteristic, and another specific frequency range The filter operation for obtaining a mixing characteristic having amplification, that is, an emphasis characteristic is not limited to this. For example, an IIR (Infinite Impulse Response) type filter calculation format may be employed. As an example of the IIR type filter calculation format, there is an example in which a feedback filter is configured using the filter processing result calculated in the previous stage and the input image data. Even in the case of FIR type filter calculation, the reference position and the number of reference pixels of the peripheral pixels including the target point are limited to 49 pixels in total in the main scanning direction and 7 pixels in the sub-scanning direction as in the above example. For example, when the resolutions of the input image in the main scanning direction and the sub-scanning direction are different, the number of reference pixels and the coefficient pairs in the main scanning direction and the sub-scanning direction when performing the convolution calculation are changed. You can also. If the target frequency band is wide, the number of reference pixels in the main scanning direction and the number of reference pixels in the sub-scanning direction will increase. The reference position of the reference pixel, the number of reference pixels, and the form of filter processing are determined in consideration of a trade-off between mounting cost (computation amount, circuit scale) and effect for obtaining the target filter characteristics. be able to.

  Next, the operation of the image processing unit 100 will be described. FIG. 9 is a flowchart illustrating an example of the procedure of the spatial filter processing of the image processing unit 100. The spatial filter processing unit 50 (hereinafter referred to as the processing unit 50) calculates a local density value of the target pixel (S11), and sets weighting coefficients (mixing ratios) α and β according to the local density value (S12).

  The processing unit 50 performs a filter operation on the input image data by the first filter operation unit 53 (S13), and performs a filter operation on the input image data by the second filter operation unit 54 (S14). In addition, the process of step S11, S12, the process of step S13, and the process of step S14 can be processed in parallel.

  The processing unit 50 multiplies the calculation result of the first filter calculation unit 53 by the coefficient α (S15), multiplies the calculation result of the second filter calculation unit 54 by the coefficient β (S16), and multiplies the calculation result by the coefficient α. And the result of multiplication by the coefficient β are added (S17), and the process is terminated.

  As described above, noise in the high density region can be suppressed, and by suppressing the noise, an increase in the average density after filtering caused by the noise can be suppressed, and deterioration in image quality can be prevented. Further, edge deterioration can be prevented in a low density region (for example, a bright portion such as a base region). Also, when the density of the input image is changed by continuously changing the weighting coefficients (mixing ratios) α and β as the density (local density value) of the pixel region transitions from the low density side to the high density side. Even if it exists, it can suppress that the density | concentration of an output image produces a sudden change, and can further prevent deterioration of an image quality.

Embodiment 2
In Embodiment 1, based on the local concentration value calculated by the local concentration calculation unit 51, the mixing ratio calculation unit 52 calculates the weighting coefficients (mixing ratios) α and β, and the first filter calculation unit 53 and the second filter Although the configuration is such that the calculation result of the calculation unit 54 is weighted, the coefficient (mixing ratio) can also be calculated based on the region separation signal indicating the region attribute of the pixel block composed of the input image or a plurality of pixels. .

  FIG. 10 is a block diagram illustrating a configuration of an image forming apparatus including the image processing apparatus according to the second embodiment. The image processing unit 100 is newly provided with a region separation processing unit 40. The region separation processing unit 40 separates each pixel in the input image into one of a character edge region, a halftone dot region, and other regions from the R, G, and B signals output from the visibility correction unit 30. is there. The region separation processing unit 40 outputs, to the spatial filter processing unit 50, the color correction unit 70, and the halftone output gradation processing unit 80, a region separation signal A indicating which region the pixel belongs to based on the separation result. In addition, the input signal output from the visibility correction unit 30 is output to the subsequent spatial filter processing unit 50 as it is.

  The region separated into characters by the region separation processing unit 40 is subjected to sharp enhancement processing in the spatial filter processing unit 50 in order to improve the character reproducibility, and the black correction amount of the black character is increased in the color correction unit 70. Processing is performed. In the halftone output gradation processing unit 80, a binary or multi-value dither method or an error diffusion method suitable for reproduction of a high frequency is selected.

  The region separated into halftone dots by the region separation processing unit 40 is subjected to smoothing processing, processing by a filter having both enhancement characteristics and smoothing characteristics, and smoothing characteristics according to the local density value of the pixels in the spatial filter processing unit 50. And a process in which the characteristics having both the emphasis characteristic and the smoothing characteristic are mixed. In the halftone output tone processing unit 80, processing using a binary or multi-value dither method or an error diffusion method with an emphasis on tone reproducibility is performed.

  FIG. 11 is a block diagram showing a configuration of the region separation processing unit 40. The area separation processing unit 40 includes a minimum density value calculation unit 41, a maximum density value calculation unit 42, a maximum density difference calculation unit 43, a total density busyness calculation unit 44, a background photographic paper / character halftone dot determination unit 45, and a maximum density difference. A threshold setting unit 46, a total density busyness threshold setting unit 47, a character edge region determination unit 48, and the like are provided.

  The minimum density value calculation unit 41 calculates a minimum density value in an n × m pixel block (for example, 15 × 15 pixels) including the target pixel, and the maximum density difference calculation unit 43 calculates n × m including the target pixel. A maximum density value in a pixel block (for example, 15 × 15 pixels) is calculated. The maximum density difference calculation unit 43 calculates a maximum density difference that is a difference between the calculated maximum density value and the minimum density value.

  The total density busyness calculation unit 44 calculates a total density busyness that is a sum of absolute values of density differences between adjacent pixels in an n × m pixel block including the target pixel (for example, 15 × 15 pixels).

  Since the density distribution of the base region is small in density change, both the maximum density difference and the total density busyness are very small. Further, the density distribution of the photographic paper photograph area (for example, a continuous tone area such as a photographic paper photograph is referred to as a photographic paper photograph area) has a smooth density change, and the maximum density difference and the total density busyness. Are both small and slightly larger than the underlying region. That is, in the background area and the photographic paper photograph area (other areas), the maximum density difference and the total density busyness are small values.

  In the density distribution of the halftone dot area, the maximum density difference varies depending on the halftone dot, but since the total density busyness varies by the number of halftone dots, the ratio of the total density busyness to the maximum density difference increases. . Therefore, when the total density busyness is larger than the product of the maximum density difference and a predetermined character / halftone determination threshold, it is possible to determine that the pixel is a halftone pixel.

  In the density distribution of the character edge area, the maximum density difference is large and the total density busyness increases accordingly. However, since the density change is smaller than that of the halftone dot area, the total density busyness is smaller than that of the halftone dot area. Therefore, when the total density busyness is smaller than the product of the maximum density difference and the predetermined character / halftone dot determination threshold, it is possible to determine that the pixel is a character edge pixel.

  The background photographic paper / character halftone dot determination unit 45 sets the calculated maximum density difference and the maximum density difference threshold set by the maximum density difference threshold setting unit 46, and sets the calculated total density busyness and total density busyness threshold. The total density busyness threshold set in the unit 47 is compared. When the maximum density difference is smaller than the maximum density difference threshold and the total density busyness is smaller than the total density busyness threshold, the background photographic paper / character halftone dot determination unit 45 sets the target pixel as the other region (background / printing). If the maximum density difference is larger than the maximum density difference threshold or the total density busyness is larger than the total density busyness threshold, the target pixel is a character / halftone dot area. judge.

  When the background photographic paper / character halftone dot determination unit 45 determines that the pixel of interest is a character / halftone dot area, the character edge area determination unit 48 determines whether the calculated total density busyness and the maximum density difference have a character / halftone character difference. Compared with the value multiplied by the point determination threshold, if the total density busyness is smaller, it is determined as a character edge region, and if the total density busyness is higher, it is determined as a halftone dot region, A determination signal (region separation signal) A is output. Through the above processing, the target pixel can be separated into a character edge region, a halftone dot region, and other regions.

  FIG. 12 is a block diagram illustrating an example of the configuration of the spatial filter processing unit 50 according to the second embodiment. Note that the local density calculation unit 51, the first filter calculation unit 53, and the weighting processing unit 55 are the same as those in the first embodiment, and thus description thereof is omitted.

  The coefficient group selection unit 56 stores a filter coefficient having a mixed characteristic having both an emphasis characteristic and a smoothing characteristic as shown in the example of FIG. 7, a filter coefficient having an emphasis characteristic described later, and the like. The coefficient set selection unit 56 selects a filter coefficient corresponding to the region separation signal A, and outputs the selected filter coefficient to the second filter calculation unit 54. For example, when the region separation signal A is a character edge region, a filter coefficient having an enhancement characteristic is selected. When the region separation signal A is a halftone region or another region (that is, other than a character edge region), A filter coefficient having smoothing characteristics is selected.

  FIG. 13 is an explanatory diagram illustrating an example of a filter coefficient having enhancement characteristics. Filter coefficients with emphasis or sharpening characteristics emphasize relatively high spatial frequency components to prevent blurring at the character edge due to weakening of high spatial frequency components compared to low spatial frequency components To do. Note that the filter coefficients in FIG. 13 are examples, and the present invention is not limited to these.

  The mixing ratio calculation unit 52 sets weighting coefficients (mixing ratios) α and β based on the local density value calculated by the local density calculation unit 51 and the region separation signal A, and weights the set weighting coefficients α and β. Output to the processing unit 55.

  FIG. 14 is an explanatory diagram showing an example of setting the weighting coefficients α and β. As shown in FIG. 14, when the region separation signal A is a character edge region, the mixture ratio calculation unit 52 sets the weighting coefficient α = 0 and the weighting coefficient β = 1 regardless of the local density value. When the region separation signal A is a character edge region, the coefficient set selection unit 56 selects a filter coefficient having an emphasis characteristic, so that the spatial filter processing result is the same as the output of the second filter operation unit 54, that is, the emphasis characteristic A filter processing result is obtained. That is, image data in which character edges are emphasized is output.

  When the region separation signal A is a halftone dot or other region (that is, other than the character edge region) and the local density value is large (for example, the local density value is 250 or more), the mixing ratio calculation unit 52 has a weighting coefficient α = 1, The weighting coefficient β = 0 is set. In this case, as the spatial filter processing result, the same filter processing result as the output of the first filter calculation unit 53, that is, smoothed image data is obtained.

  When the region separation signal A is a halftone dot or other region (that is, other than a character edge region) and the local density value is small (for example, the local density value is 105 or less), the mixture ratio calculation unit 52 has a weighting coefficient α = 0, The weighting coefficient β = 1 is set. When the region separation signal A is a halftone dot or other region, the coefficient group selection unit 56 selects a filter coefficient having both enhancement characteristics and smoothing characteristics, so that filter processing having enhancement characteristics and smoothing characteristics is performed.

  When the region separation signal A is a halftone dot or other region (that is, other than a character edge region) and the local density value is an intermediate value (for example, the local density value is 105 to 250), the mixture ratio calculation unit 52 calculates the weighting coefficient α = 1-a and weighting coefficient β = a (0 <a <1) are set. When the region separation signal A is a halftone dot or other region, the coefficient set selection unit 56 selects a filter coefficient having both enhancement characteristics and smoothing characteristics, so that the second filter calculation unit 54 enhances the density according to the density value. A process is performed in which the filter calculation using the filter coefficient having both characteristics and smoothing characteristics and the calculation result using the filter coefficient having the smoothing characteristic are mixed in the first filter calculation unit 53.

  As described above, the halftone dot region and other regions are included in the input image data by performing smoothing in the high density region of the halftone dot region and other regions by switching the filtering method according to the local density value. Amplification of noise is suppressed, and in the low density region of the halftone dot region and other regions, by performing filter processing with emphasis characteristics and smoothing characteristics, it is unnecessary while improving the MTF characteristics for low frequency components. High frequency components can be removed.

  Next, the operation of the image processing unit 100 according to the second embodiment will be described. FIG. 15 is a flowchart illustrating an example of a spatial filter processing procedure of the image processing unit 100 according to the second embodiment. The spatial filter processing unit 50 (hereinafter referred to as the processing unit 50) calculates the local density value of the pixel of interest (S21), and sets weighting coefficients (mixing ratios) α and β according to the local density value and the region separation signal. (S22).

  The processing unit 50 performs a filter operation on the input image data by the first filter operation unit 53 (S23), selects a filter coefficient based on the region separation signal (S24), and selects the input image data. Using the filter coefficient, the second filter calculation unit 54 performs filter calculation (S25). In addition, the process of step S21, S22, the process of step S23, and the process of step S24, S25 can be processed in parallel.

  The processing unit 50 multiplies the calculation result of the first filter calculation unit 53 by the coefficient α (S26), multiplies the calculation result of the second filter calculation unit 54 by the coefficient β (S27), and multiplies the calculation result by the coefficient α. And the result of multiplication of the coefficient β are added (S28), and the process is terminated.

  As described above, by excluding the character edge region when performing the smoothing process, the noise amplified in the high density region can be suppressed by the smoothing process while preventing the deterioration of the character edge, Image quality deterioration can be prevented. In addition, by performing a filter process having an emphasis characteristic on the character edge area, or by applying a filter process having an emphasis characteristic and a smooth characteristic according to the local density value in an area other than the character edge area, Edges can be preserved to prevent edge degradation. Also, when the density of the input image is changed by continuously changing the weighting coefficients (mixing ratios) α and β as the density (local density value) of the pixel region transitions from the low density side to the high density side. Even if it exists, it can suppress that the density | concentration of an output image produces a sudden change, and can further prevent deterioration of an image quality.

Embodiment 3
In the first and second embodiments described above, the spatial filter processing unit 50 is configured to include the two filter calculation units of the first filter calculation unit 53 and the second filter calculation unit 54. The configuration is not limited to this, and a plurality of filter coefficients can be mixed to generate one filter coefficient, and the generated filter coefficient can be used to share a single filter operation unit.

  FIG. 16 is a block diagram illustrating an example of the configuration of the spatial filter processing unit 50 according to the third embodiment. In the third embodiment, the spatial filter processing unit 50 includes a local density calculation unit 51, a mixture ratio calculation unit 52, a first coefficient set donating unit 57, a second coefficient set donating unit 58, a weighting processing unit 59, and a filter calculation unit 501. The weighting processing unit 59 includes multipliers 591 and 592 and an adder 593. The local concentration calculation unit 51 and the mixture ratio calculation unit 52 are the same as those in the first and second embodiments, and thus the description thereof is omitted.

  The first coefficient set providing unit 57 stores filter coefficients having smooth characteristics, and the second coefficient set providing unit 58 is a filter coefficient different from the filter coefficients stored in the first coefficient set providing unit 57, for example, The filter coefficient of the mixed characteristic having both the emphasis characteristic and the smoothing characteristic is stored.

  The weighting processing unit 59 multiplies the filter coefficient extracted from the first coefficient group providing unit 57 by the weighting coefficient α calculated by the mixing ratio calculating unit 52 and the filter coefficient extracted from the second coefficient group providing unit 58 to the mixing ratio. The weighting coefficient β calculated by the calculation unit 52 is multiplied, and the filter coefficients multiplied by the weighting coefficients α and β are added to generate one filter coefficient (mixed filter coefficient), and the generated filter coefficient is filtered. Output to the unit 501.

  The filter calculation unit 501 performs a convolution calculation with input image data using a filter coefficient generated by mixing two types of filter coefficients.

  Here, a method of mixing filter coefficients will be described. FIG. 17 is an explanatory diagram showing a configuration example of the spatial filter kernel. As shown in FIG. 17, the spatial filter kernel has a size corresponding to the reference pixel region, and coefficients L (−H, −V),..., L (0, 0),. (H, V).

  The configuration of the two filter coefficients to be mixed is, for example, the example of FIG. 5 and the example of FIG. 17 described above. ) To apply the filtering process. Assuming that O (x, y) is the filter processing result for the target pixel, O (x, y) can be expressed by Expression (2).

  Here, when the filter coefficient is normalized in advance so as to satisfy Expression (3), O (x, y) can be expressed by Expression (4).

  The process of applying the filter coefficient to the input image data I (i, j) at a ratio of α: β (α + β = 1) is performed by mixing the filter coefficient at α: β and creating a new filter. This is equivalent to a case where a coefficient is generated and filter processing is performed using the generated filter coefficient.

  Next, the operation of the image processing unit 100 will be described. FIG. 18 is a flowchart illustrating an example of the procedure of the spatial filter processing of the image processing unit 100 according to the third embodiment. The spatial filter processing unit 50 (hereinafter referred to as the processing unit 50) calculates a local density value of the pixel of interest (S31), and sets weighting coefficients (mixing ratios) α and β according to the local density value (S32).

  The processing unit 50 multiplies the filter coefficient extracted from the first coefficient set providing unit 57 by the coefficient α (S33), and multiplies the filter coefficient extracted from the second coefficient set providing unit 58 by the coefficient β (S34). The processing unit 50 adds the filter coefficient multiplied by the coefficient α and the filter coefficient multiplied by the coefficient β (S35), performs a filter operation using the added filter coefficient (S36), and ends the process.

  As described above, a single filter operation unit is generated (shared) and a plurality of filter coefficients are mixed to generate one filter coefficient according to the density (local density value) of the pixel region. For example, it is possible to reduce the amount of processing compared to a case where a plurality of filter calculation units are provided and processing is performed by each filter calculation unit. In particular, when the filter size (filter matrix size) is large, the amount of processing can be further reduced. In addition, when the number of filter coefficient types is small, the processing amount can be optimized.

Embodiment 4
In the third embodiment described above, the second coefficient group provision unit 58 is configured to store filter coefficients different from the filter coefficients having the smoothing characteristics stored in the first coefficient group provision unit 57. However, the present invention is not limited to this. The second coefficient set providing unit 58 stores a plurality of filter coefficients, and selects one filter coefficient from the stored filter coefficients in accordance with the region separation signal A, and the mixing ratio. The weighting coefficients (mixing ratios) α and β calculated by the calculation unit 52 can also be set according to the region separation signal A.

  FIG. 19 is a block diagram illustrating an example of the configuration of the spatial filter processing unit 50 according to the fourth embodiment. The configuration of the spatial filter processing unit 50 is the same as that of the third embodiment, except that the region separation signal A is output to the mixing ratio calculation unit 52 and the second coefficient group provision unit 58.

  The mixing ratio calculation unit 52 calculates weighting coefficients (mixing ratios) α and β based on the local concentration value and the region separation signal A output from the local concentration calculation unit 51. The calculation method is the same as in the second embodiment.

  Similar to the coefficient group selection unit 56 of the second embodiment (FIG. 12), the second coefficient group provision unit 58 is a mixed coefficient filter coefficient having both enhancement characteristics and smoothing characteristics as shown in the example of FIG. A filter coefficient having an emphasis characteristic as shown in the example of FIG. 13 is stored. The second coefficient set provision unit 58 selects a filter coefficient corresponding to the region separation signal A and outputs the selected filter coefficient to the weighting processing unit 59. For example, when the region separation signal A is a character edge region, a filter coefficient having an enhancement characteristic is selected. When the region separation signal A is a halftone region or another region (that is, other than a character edge region), A filter coefficient having smoothing characteristics is selected.

  Since the first coefficient set provision unit 57, the weighting processing unit 59, and the filter calculation unit 501 are the same as those in the third embodiment, description thereof is omitted.

  Next, the operation of the image processing unit 100 will be described. FIG. 20 is a flowchart illustrating an example of the procedure of the spatial filter processing of the image processing unit 100 according to the fourth embodiment. The spatial filter processing unit 50 (hereinafter referred to as the processing unit 50) calculates the local density value of the target pixel (S41), and sets weighting coefficients (mixing ratios) α and β according to the local density value and the region separation signal. (S42).

  The processing unit 50 multiplies the filter coefficient extracted from the first coefficient group providing unit 57 by the coefficient α (S43), and selects the filter coefficient of the second coefficient group providing unit 58 according to the region separation signal (S44). The selected filter coefficient taken out from the second coefficient set providing unit 58 is multiplied by a coefficient β (S45). The processing unit 50 adds the filter coefficient multiplied by the coefficient α and the filter coefficient multiplied by the coefficient β (S46), performs a filter operation using the added filter coefficient (S47), and ends the process.

  As described above, by excluding the character edge region when performing the smoothing process, the noise amplified in the high density region can be suppressed by the smoothing process while preventing the deterioration of the character edge, Image quality deterioration can be prevented. In addition, by performing a filter process having an emphasis characteristic on the character edge area, or by applying a filter process having an emphasis characteristic and a smooth characteristic according to the local density value in an area other than the character edge area, Edges can be preserved to prevent edge degradation. Also, when the density of the input image is changed by continuously changing the weighting coefficients (mixing ratios) α and β as the density (local density value) of the pixel region transitions from the low density side to the high density side. Even if it exists, it can suppress that the density | concentration of an output image produces a sudden change, and can further prevent deterioration of an image quality.

Embodiment 5
In the above-described embodiment, the weighting coefficients (mixing ratios) α and β calculated by the mixing ratio calculation unit 52 when the local density value is D2 are configured such that α = 1 and β = 0. It is not limited.

  FIG. 21 is an explanatory diagram showing another example of the weighting coefficients α and β. In FIG. 21, the horizontal axis represents the local density value calculated by the local density calculation unit 51, and the vertical axis represents the weighting value. As shown in FIG. 21, when the local density value is D2, the weighting coefficient α <1 and the weighting coefficient β> 0 are set. As a result, the calculation result of the smoothing process in the first filter calculation unit 53 or the filter calculation process result using the filter coefficient of the first coefficient set providing unit 57 and the calculation in the second filter calculation unit 54 or The mixed output with the processing result of the filter calculation using the filter coefficient of the second coefficient group providing unit 58 is performed even at the maximum density.

  As an advantage in the configuration of the fifth embodiment, it can be mentioned that the calculation in the first filter calculation unit 53 or the filter calculation using the filter coefficient of the first coefficient group providing unit 57 may be simplified.

  FIG. 22 is an explanatory diagram showing an example of a filter coefficient having smoothing characteristics. In the case of a filter coefficient as shown in FIG. 22, the filter coefficient values are only 1 and 0, and there are few types of coefficient values. For this reason, the processing amount required for the filter operation can be reduced. The calculation in the first filter calculation unit 53 using filter coefficients having strong smoothing characteristics as in the example of FIG. 22 or the filter calculation using the filter coefficients as in the example of FIG. And the processing result of the filter operation using the filter coefficient of the second coefficient set providing unit 58 are weakened by mixing the calculation result of the above and the result of the calculation by the second filter calculation unit 54 or the filter coefficient of the second coefficient set providing unit 58. Smoothing results can be obtained.

Embodiment 6
In the above-described embodiment, the local density value is calculated for each of R, G, and B to calculate the weighting coefficients (mixing ratios) α and β. However, the present invention is not limited to this. A configuration is also possible in which local density values of a plurality of colors of R, G, and B are calculated, and weighting coefficients α and β common to the respective colors are calculated based on the calculated local density values.

  FIG. 23 is a block diagram illustrating an example of the configuration of the spatial filter processing unit 50 according to the sixth embodiment, and FIG. 24 is a block diagram illustrating an example of the configuration of each of the color space filter units 504, 505, and 506. As shown in FIG. 23, the spatial filter processing unit 50 according to the sixth embodiment includes a common local density calculation unit 502, a common mixture ratio calculation unit 503, and color space filter units 504, 505, and 506.

  As shown in the example of FIG. 3, the common local density calculation unit 502 uses a coefficient set in which all the same weighting coefficients (1 in the example of FIG. 3) are assigned to a pixel area of 5 pixels × 5 pixels. After the convolution calculation is performed every time, a weighted average value is calculated by adding a weight for each color to calculate a local density value. For example, in the case of three colors of R, G, and B, the weight of each color of R, G, and B is 0.30, 0.58, and 0.12, respectively. Corresponding density values can be obtained.

  The common mixture ratio calculation unit 503 calculates and calculates common weighting coefficients (mixing ratios) α and β for each color according to the local density value calculated by the common local density calculation unit 502 and the region separation signal A. The weighting coefficients α and β are output to the color space filter units 504, 505, and 506, respectively.

  Each color space filter unit 504 includes a first filter calculation unit 53, a second filter calculation unit 54, a coefficient set selection unit 56, and a weighting processing unit 55. The configurations of the first filter calculation unit 53, the second filter calculation unit 54, the coefficient group selection unit 56, and the weighting processing unit 55 are the same as those in the example of FIG. Each color space filter unit 505 and 506 has the same configuration as each color space filter unit 504.

  With the above configuration, common filter processing can be applied to all colors by using common weighting coefficients (mixing ratios) α and β for each color. Thus, different filter processing is not performed for each color during color mixing, and a difference in image quality between color mixing and non-color mixing can be prevented.

Embodiment 7
In the above-described embodiment, the first filter calculation unit 53 and the second filter calculation unit 54 are provided in parallel, and the same input image data is input and calculated individually. However, the present invention is not limited to this. Alternatively, the first filter calculation unit 53 and the second filter calculation unit 54 may be provided in series.

  FIG. 25 is a block diagram illustrating an example of the configuration of the spatial filter processing unit 50 according to the seventh embodiment. The spatial filter processing unit 50 according to the seventh embodiment includes a local density calculation unit 51, a first coefficient group selection unit 561, a second coefficient group selection unit 562, a first filter calculation unit 53, a second filter calculation unit 54, and the like. ing.

  The first coefficient group selection unit 561 stores a plurality of sets of filter coefficients in advance, selects a filter coefficient according to the local density value calculated by the local density calculation unit 51 and the region separation signal A, and selects the selected filter coefficient. Is output to the first filter calculation unit 53.

  26 is an explanatory diagram illustrating an example of a filter coefficient selection method, FIG. 27 is an explanatory diagram illustrating an example of a first filter coefficient, and FIG. 28 is an explanatory diagram illustrating an example of a second filter coefficient. 29 is an explanatory diagram showing an example of the third filter coefficient. As shown in FIG. 26, when the region separation signal A is a halftone dot or other region (that is, other than the character edge region), the first coefficient set selection unit 561 smoothes the smoothing characteristic according to the local density value. The first filter coefficient (see FIG. 27), the second filter coefficient (see FIG. 28), the third filter coefficient (see FIG. 29), and the fourth filter coefficient (see FIG. 6) having different degrees of conversion are selected. . Thereby, for example, as the local density value increases (high density), the degree of smoothing is increased. Thereby, while suppressing the noise in a high density area | region, the increase in the average density after the filter process resulting from noise can be suppressed, and image quality degradation can be prevented.

  Further, when the region separation signal A is a character edge region, the first coefficient set selection unit 561 selects the first filter coefficient (see FIG. 27) regardless of the local density value, and does not perform the smoothing process. To.

  When the region separation signal A is a halftone dot or other region (that is, other than the character edge region), the second coefficient group selection unit 562 selects a filter coefficient for performing the filtering process of the mixed characteristic as shown in FIG. If the region separation signal A is a character edge region, a filter coefficient for performing filter processing having emphasis characteristics as shown in FIG. 13 is selected, and the selected filter coefficient is output to the second filter calculation unit 54. .

  The first filter calculation unit 53 performs a filter calculation on the input image data using the filter coefficient selected by the first coefficient set selection unit 561 and outputs the calculated image data to the second filter calculation unit 54. .

  The second filter calculation unit 54 performs a filter calculation on the image data output from the first filter calculation unit 53 using the filter coefficient selected by the second coefficient set selection unit 562.

  As described above, the first coefficient set selection unit 561 selects one filter coefficient according to the local density value and the region separation signal A from the plurality of filter coefficients having smoothing characteristics, and selects the second coefficient set. The unit 562 selects one filter coefficient according to the region separation signal A from among a plurality of filter coefficients having a mixing characteristic obtained by combining the enhancement characteristic and the smoothing characteristic. As a result, the first filter calculation unit 53, which is the preceding filter processing unit, removes noise having a certain frequency or higher, and then the second filter calculation unit 54 performs the optimum filter processing corresponding to a specific image region. Can do. Note that the order of processing can be changed by exchanging the first filter calculation unit 53 and the second filter calculation unit 54.

  Next, the operation of the image processing unit 100 according to the seventh embodiment will be described. FIG. 30 is a flowchart illustrating an example of a spatial filter processing procedure of the image processing unit 100 according to the seventh embodiment. The spatial filter processing unit 50 (hereinafter referred to as the processing unit 50) calculates a local density value of the target pixel (S51), and selects a filter coefficient of the first coefficient group according to the local density value and the region separation signal (S52). ).

  The processing unit 50 performs calculation in the first filter calculation unit 53 using the selected filter coefficient (S53), and outputs the calculation result to the second filter calculation unit 54. The processing unit 50 selects a filter coefficient of the second coefficient group according to the region separation signal (S54), and uses the selected filter coefficient to perform the second filter calculation unit 54 on the calculation result of the first filter calculation unit 53. In step S55, the process is terminated.

Embodiment 8
In the above-described embodiment, each unit (each functional block) constituting the spatial filter processing unit 50 and the control unit (not shown) provided in the digital color copying machine (or multifunction machine) uses a processor such as a CPU. It can also be realized by software. That is, a digital copying machine (or a multifunction machine) includes a central processing unit (CPU) that executes instructions of a control program that realizes each function, a ROM (read only memory) that stores the program, and a RAM ( random access memory), a storage device (recording medium) such as a memory for storing the program and various data.

  The present invention provides a recording medium in which a program code (execution format program, intermediate code program, source program) of a control program for a digital copying machine (or multifunction machine), which is software that realizes the above-described functions, is recorded in a computer-readable manner. This can be achieved by supplying the program to a digital copying machine (or a multifunction machine) and reading and executing the program code recorded on the recording medium by the computer (or CPU, MPU, etc.).

  Examples of the recording medium include a tape system such as a magnetic tape and a cassette tape, a magnetic disk such as a flexible disk or a hard disk, a disk system including an optical disk such as a CD-ROM / MO / MD / DVD / CD-R, and an IC card. A card system such as an optical card (including a memory card) or a semiconductor memory system such as a mask ROM / EPROM / EEPROM / flash ROM can be used.

  Further, a digital copying machine (or a multifunction machine) may be configured to be connectable to a communication network, and the program code may be supplied via the communication network. The communication network is not particularly limited. For example, the Internet, intranet, extranet, LAN, ISDN, VAN, CATV communication network, virtual private network, telephone line network, mobile communication network Satellite communication networks can be used. In addition, the transmission medium constituting the communication network is not particularly limited, and for example, it can be used by wired lines such as IEEE 1394, USB, power line carrier, cable TV line, telephone line, ADSL line, and IrDA. Alternatively, it can also be used by wireless such as infrared rays such as remote control, Bluetooth (registered trademark), 802.11 wireless, HDR, mobile phone network, satellite line, terrestrial digital network.

  The present invention can also be realized in the form of a computer data signal embedded in a carrier wave in which the program code is embodied by electronic transmission. In addition, each functional block of the digital copying machine (or multifunction device) is not limited to being realized using software, and may be configured by hardware logic, and hardware that performs a part of processing. And a calculation means for executing software for controlling the hardware and performing the remaining processing may be used.

  The present invention includes an image input device such as a flatbed scanner, a film scanner, and a digital camera, a computer that performs the above-described processes by loading a predetermined program, a CRT display or a liquid crystal display that displays the processing results of the computer, etc. The image forming apparatus may be configured by an image display apparatus and an image forming apparatus such as a printer that outputs the processing result of the computer to paper or the like. In addition, a network card, a modem, or the like as a communication means for connecting to a server or the like via a network can be provided.

  The filter coefficients shown in the above-described embodiments are examples, and the present invention is not limited to this. Filter coefficients having different coefficient values and filter coefficients having different sizes can be used as appropriate.

DESCRIPTION OF SYMBOLS 10 A / D conversion part 20 Shading correction | amendment part 30 Visibility correction | amendment part 40 Area | region separation process part 50 Spatial filter process part 51 Local density | concentration calculation part 52 Mixing ratio calculation part 53 1st filter calculation part 54 2nd filter calculation part 55, 59 Weighting processing unit 551, 552, 591, 592 Multiplier 553, 593 Adder 56 Coefficient group selection unit 561 First coefficient group selection unit 562 Second coefficient group selection unit 57 First coefficient group provision unit 58 Second coefficient group provision unit 501 Filter calculation unit 502 Common local density calculation unit 503 Common mixing ratio calculation unit 504, 505, 506 Each color space filter unit 60 Scaling processing unit 70 Color correction unit 80 Halftone output tone processing unit 100 Image processing unit 110 Image input unit 120 Image output unit 130 Operation panel

Claims (9)

In an image processing apparatus that performs image processing on input image data,
Density calculating means for calculating the density of the pixel area based on the pixel value of the pixel area constituted by a plurality of pixels of the input image;
A first processing means for performing a smoothing process on input image data when the density calculated by the density calculation means is greater than a predetermined first density threshold;
When the density calculated by the density calculation unit is smaller than a predetermined second density threshold (<the first density threshold) , the input image data is enhanced, mixed with enhancement and smoothing, or strongly smoothed. A second processing means for performing any one of the processing combining the processing and the weak smoothing processing;
An image processing apparatus comprising: weighting processing means for performing weighting processing on image data processed by each of the first processing means and the second processing means in accordance with the density calculated by the density calculation means. In an image processing apparatus that performs digital filter arithmetic processing on input image data,
Storage means for storing a first filter coefficient for performing smoothing processing on input image data;
Stores a second filter coefficient for performing any of enhancement processing, mixing processing of enhancement processing and smoothing processing, or processing combining strong smoothing processing and weak smoothing processing on input image data. Storage means for
Density calculating means for calculating the density of the pixel area based on the pixel value of the pixel area constituted by a plurality of pixels of the input image;
A weighting coefficient for setting a first coefficient for weighting the first filter coefficient and a second coefficient for weighting the second filter coefficient in accordance with the density calculated by the density calculation means Setting means ;
Using the first coefficient set in the previous Kiomomi coefficient setting means, performs weighting on the first filter coefficients, performs weighting on the second filter coefficients using the second coefficient were performed respectively weighting Weighting processing means for adding filter coefficients to generate new filter coefficients ;
An image processing apparatus comprising: an operation unit that performs a filter operation on input image data using a new filter coefficient generated by the weighting processing unit . The concentration calculating means includes
The density is calculated based on the pixel values of the pixel areas of each of the plurality of specific colors,
The weighting processing means includes
The image processing apparatus according to claim 1, wherein a common weighting process is performed on each of the specific colors according to the density calculated by the density calculation unit.   An image forming apparatus comprising: the image processing apparatus according to claim 1; and an image forming unit that forms an image processed by the image processing apparatus. In an image processing method for performing image processing on input image data,
Calculating the density of the pixel area based on the pixel value of the pixel area composed of a plurality of pixels of the input image;
A first processing step of performing a smoothing process on the input image data when the density calculated in the step of calculating the density is greater than a predetermined first density threshold;
When the density calculated in the step of calculating the density is smaller than a predetermined second density threshold (<the first density threshold) , the input image data is enhanced, mixed with enhancement and smoothing, Or a second processing step for performing any one of the processes of combining a strong smoothing process and a weak smoothing process;
And a step of weighting the image data processed in each of the first processing step and the second processing step according to the density calculated in the step of calculating the density. . In an image processing method for performing digital filter arithmetic processing on input image data,
A first filter coefficient for smoothing the input image data is stored;
Stores a second filter coefficient for performing any of enhancement processing, mixing processing of enhancement processing and smoothing processing, or processing combining strong smoothing processing and weak smoothing processing on input image data. Aside,
Calculating the density of the pixel area based on the pixel value of the pixel area composed of a plurality of pixels of the input image;
A first coefficient for weighting the first filter coefficient and a second coefficient for weighting the second filter coefficient are set according to the density calculated in the step of calculating the density. And steps to
A filter that weights the first filter coefficient using the first coefficient set in the setting step, weights the second filter coefficient using the second coefficient, and weights the second filter coefficient. Adding a coefficient to create a new filter coefficient;
An image processing method comprising: performing a filter operation on input image data using the filter coefficient created in the creating step. In a computer program for causing a computer to perform image processing on input image data,
Computer
Density calculating means for calculating the density of the pixel area based on the pixel value of the pixel area constituted by a plurality of pixels of the input image;
A first processing means for performing a smoothing process on input image data when the density calculated by the density calculation means is greater than a predetermined first density threshold;
When the density calculated by the density calculation unit is smaller than a predetermined second density threshold (<the first density threshold) , the input image data is enhanced, mixed with enhancement and smoothing, or strongly smoothed. A second processing means for performing any one of the processing combining the processing and the weak smoothing processing;
A computer program that functions as weighting processing means for applying weighting processing to image data processed by each of the first processing means and the second processing means in accordance with the density calculated by the density calculation means. In a computer program that causes a computer to perform digital filter arithmetic processing on input image data,
Computer
Density calculating means for calculating the density of the pixel area based on the pixel value of the pixel area constituted by a plurality of pixels of the input image;
A first coefficient for weighting the first filter coefficient for smoothing the input image data according to the density calculated by the density calculation means, and an enhancement process for the input image data; A second coefficient for weighting the second filter coefficient for performing any one of the mixing process of the enhancement process and the smoothing process or the process combining the strong smoothing process and the weak smoothing process; a weighting factor setting means for setting a,
Using the first coefficient set in the previous Kiomomi coefficient setting means, performs weighting on the first filter coefficients, performs weighting on the second filter coefficients using the second coefficient were performed respectively weighting Weighting processing means for adding filter coefficients to generate new filter coefficients ;
The input image data, a computer program for causing to function as a calculating means for performing a filter operation using the new filter coefficients generated by the weighting processing means.   9. A computer-readable recording medium in which the computer program according to claim 7 or 8 is recorded.

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