JP4771428B2 - Image processing apparatus, image processing method, program, and recording medium - Google Patents

Description

  The present invention relates to an image processing apparatus, an image processing method, a program, and a recording medium for identifying a character area on an image read by a scanner or an image acquired via a network and performing optimum processing according to the identification result About.

  A device that identifies a white ground character area and a pattern area (in this case, an area other than a white ground character area) from a scanner read image, and switches image processing such as filter, black ink processing, and pseudo gradation processing according to the identification result. is there. Such switching of image processing is performed in order to reproduce characters sharply and to reproduce a pattern with reduced roughness. Originally, characters on a halftone dot background are as sharp as white ground characters. Although it is desired to reproduce, since it is difficult to distinguish the character on the halftone dot from the edge in the halftone dot pattern, it is processed as a picture area in consideration of the influence on the picture part.

  For example, in Patent Documents 1 and 2, a character area and a picture area are identified without distinguishing white ground characters and half-tone characters. In both cases, the image is binarized and the character is identified by the continuity of the binary signal. However, especially when the halftone dot pattern edge is relatively steep, the character edge on the halftone dot is separated from the halftone dot pattern edge. Cannot be identified.

  FIG. 31A shows a binary image binarized by the threshold th1, and FIG. 31B shows a binary image binarized by the threshold th2 on the higher density side than the threshold th1. Even for a character edge on a halftone dot, by appropriately setting the threshold value, it is possible to binarize the character portion as a black pixel and the halftone dot background as a white pixel as shown in FIG. Even when the background of the halftone dot is relatively high, due to the characteristics of the scanner, the ridge density of the line drawing tends to be higher than the peak density of the halftone dot, and in most cases the threshold value is the threshold value. Can be set appropriately, a binarized result as shown in FIG.

  In FIG. 31 (b), the identification of the edge in the halftone dot pattern and the character edge on the halftone dot becomes a problem, but the difference between the two is that the edge in the halftone dot pattern has local irregularities in the edge portion. On the other hand, the character edge on the halftone dot is composed of a smooth edge along the original character shape even when viewed locally, and there is very little local unevenness. The horizontal line or vertical line is relatively uneven due to noise at the time of scanner reading, but it is still about 1 dot uneven.

  In the case of the above-described Patent Documents 1 and 2, even if there is local unevenness, it is determined as a character edge. Patent Document 2 is different from Patent Document 1 in that the binarization process is a binarization process in which only the edge portion is extracted as a black pixel. Therefore, the character region is extracted from the binary image as shown in FIG. However, in the case of a relatively steep pattern edge in which black pixels are connected at the edge portion, it is identified as a character region regardless of local unevenness. At the time when Patent Documents 1 and 2 were invented, the resolution of the scanner was much lower than it is now, and there was also a situation that it was difficult to identify the local shape of the edge part because the halftone dot shape was not read accurately .

JP-A-2-292957 JP-A-1-227573 JP 2002-199210 A

  However, with the recent increase in resolution of scanners, a technique for consciously extracting characters on halftone dots has been proposed. For example, in Patent Document 3, a character on a halftone dot is extracted, but a character edge on a halftone dot and an edge in a halftone dot pattern are not separated and identified.

  An object of the present invention is to perform image processing that distinguishes a character edge on a halftone dot from an edge in a halftone dot pattern by focusing on local unevenness of the edge and determining whether the edge is smooth or not. An apparatus, an image processing method, a program, and a recording medium are provided.

In the present invention, the difference between the average value of two pixel values at symmetrical positions across the center pixel of the n pixels × n pixel region constituting the image and the pixel value of the center pixel is larger than a predetermined threshold value. Sometimes, a peak pixel detecting means for detecting the center pixel as a peak pixel, a setting means for setting a threshold value based on a pixel value of the detected peak pixel, and binarizing the image using the threshold value From the binarization means and the binarized image, upper left vertical edge, lower left vertical edge, upper right vertical edge, lower right vertical edge, upper left horizontal edge, upper right horizontal edge, lower left horizontal edge, lower right horizontal edge are extracted. And extracting means for detecting when the extracted edge is an upper left vertical edge and a lower left vertical edge as a continuous edge, and the extracted edge is an upper left vertical edge and an upper right vertical edge, or a lower left vertical edge and a lower right vertical edge. Edge field , Detected as Peaejji, if the extracted edges of the upper right lateral edge and the lower left vertical edge, an edge detecting means for detecting as the intersection edge, the character a character edge on halftone dots based on a result of detection by the edge detecting means The main feature is to have an identification means for identifying as an edge.

According to the present invention, when image processing is switched using an identification result that identifies a character edge on a halftone dot as a character edge, the optimum image processing is effectively performed on the character edge without impairing the picture quality. Can be applied.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

Example 1:
FIG. 1 shows the configuration of Embodiment 1 of the present invention. The scanner 10 obtains image data composed of RGB signals by reading a document, and the scanner γ correction unit 11 corrects the γ characteristics of the scanner, and converts the reflectance linear signal into a density linear signal. The scanner color correction 12 converts the RGB signal depending on the scanner characteristics into a device-independent R′G′B ′ signal that does not depend on the scanner.

  The image attribute determination unit 20 extracts a character edge region from the RGB signal. The character edge area includes white ground characters and half-dot characters. The edge amount extraction unit 30 extracts an edge amount that is a signal representing the likelihood of an edge in the image from the R′G′B ′ signal. Based on the results of the image attribute determination unit 20 and the edge amount extraction unit 30, the filter processing unit 13 performs smoothing processing that suppresses the undulation of the halftone portion and suppresses moire, and edge enhancement processing that increases the sharpness of the character portion I do. The printer color correction unit 14 performs conversion from a device-independent R'G'B 'signal to a C'M'Y' signal depending on printer characteristics.

  The edge amount extraction means 40 extracts an edge amount that is a signal representing the likelihood of an edge in the image from the C′M′Y ′ signal. The UCR / black generation 15 means generates a K signal according to the C′M′Y ′ data based on the results of the image attribute determination means 20 and the edge amount extraction means 40 (black generation), and C′M′Y ′. The amount corresponding to K is reduced (under color removal (UCR)). CMYK corresponds to the color material color of the printer. The signal after the UCR / black generation is processed by the printer γ correction unit 16 and the pseudo gradation processing unit 17, and the printer 18 outputs an image on a recording medium. The printer γ correction unit 16 performs a conversion process using a density conversion table in accordance with the density characteristics of the printer. The pseudo gradation processing means 17 performs pseudo halftone processing such as dithering and error diffusion.

  FIG. 2 shows the configuration of the image attribute determination means 20. The RGB → CMY conversion unit 201 converts the CMY signal corresponding to the process color of the original, binarizes the CMY signal (202, 212, 222), and determines the smoothness of the edge (203, 213, 223). , Continuous edge detection (204, 214, 224) / pair edge detection (205, 215, 225) / intersection edge detection (206, 216, 226), character pattern identification (207, 217, 227). Perform a logical OR operation on the character edges identified from the signal. If it is identified as a character edge by any one of the signals Cmy, the result of the image attribute determination means 20 becomes a character edge.

  The RGB → CMY conversion unit 201 converts the R′G′B ′ signal into a cmy signal corresponding to the process color of the document by the following equation (1).

c = a0 + a1 × R + a2 × G + a3 × B
m = b0 + b1 × R + b2 × G + b3 × B
y = c0 + c1 * R + c2 * G + c3 * B (Formula 1)
a0 to a3, b0 to b3, and c0 to c3 are parameters set in advance based on the relationship between the RGB read value and the color of the color patch when the process color patch is read by the scanner.

FIG. 3A shows the configuration of the binarizing means 202, 212, 222. In the peak pixel detection, it is determined from the density relationship with surrounding pixels whether or not the target pixel is an extreme point indicating a peak of density change. When the density level of the central pixel is higher than all other density levels in the block composed of M × M pixels, it is determined whether or not it is an extreme point according to Expression 2 or Expression 3. The peak pixel detection means (3 × 3) 231 detects the peak pixel according to Expression 2, and the peak pixel detection means (5 × 5) 232 detects the peak pixel according to Expression 3.
When M = 3 (FIG. 3B),
(2m0-m1-m8) ≧ ΔmTH and
(2m0−m2−m7) ≧ ΔmTH and
(2m0−m3−m6) ≧ ΔmTH and
(2m0-m4-m5) ≧ ΔmTH (Formula 2)
In the case of M = 5 (FIG. 3C),
(2m0-m1-m24) ≧ ΔmTH and
(2m0-m7-m18) ≧ ΔmTH and
(2m0−m3−m22) ≧ ΔmTH and
(2m0-m8-m17) ≧ ΔmTH and
(2m0-m5-m20) ≧ ΔmTH and
(2m0-m9-m16) ≧ ΔmTH and
(2m0-m11-m14) ≧ ΔmTH and
(2m0−m12−m13) ≧ ΔmTH (Formula 3)
That is, the center pixel is detected as a peak when the density difference between the average value of the two pixel levels located symmetrically with respect to the center pixel and the density of the center pixel is larger than the threshold value ΔmTH. If either one of the two peak pixel detection means 231 and 232 is detected as a peak pixel in the OR circuit 233, it is determined as a peak pixel. The threshold value setting means 234 has the highest pixel value (highest density) among the peak pixels from the peak pixel and the pixel value (c or m or y value) within the 9 × 9 pixel centering on the target pixel. And threshold value = peak pixel maximum value + α. α is a parameter for giving a margin to the threshold, and when the CMY signal is 8 bits each, α is set to a value of about 10 to 30. The white pixel / black pixel determining unit 235 determines that the pixel is a black pixel if the value of c (or m or y) is equal to or greater than a set threshold value, and is a white pixel if the value is less than the set threshold value. As a result, the halftone dot character is determined to be a white pixel, and the character portion (with the exception of white characters on the halftone dot) has a higher density than the halftone dot, so it is determined to be a black pixel. Is done.

  FIG. 4A shows the configuration of the edge smoothness determination means 203, 213, and 223. P1 pattern matching 241 is P1 in FIG. 4B, P2 pattern matching 242 is P2 in FIG. 4B, P3 pattern matching 243 is P3 in FIG. 4B, and P4 pattern matching 244 is in FIG. 4B. Pattern matching with a 3 × 3 size binary image pattern of P4 is performed, and “1” is output if they match, and “0” is output if they do not match.

  Next, the number of pixels in the horizontal direction or the vertical direction is counted for each output value, and the vertical edge or the horizontal edge is extracted from the region in the four directions viewed from the target pixel. Extracted here are eight binary signals: upper left vertical edge / lower left vertical edge / upper right vertical edge / lower right vertical edge / upper left horizontal edge / upper right horizontal edge / lower left horizontal edge / lower right horizontal edge.

  As shown in FIG. 5A-2, the reference area is divided into four equal parts with the pixel of interest at the center, and when there is a left edge (rising edge) of the vertical line in the upper left area, the upper left vertical edge and the left side of the vertical line in the lower left area When there is an edge (rising edge), it is extracted as a lower left vertical edge.

  As shown in FIGS. 5 (a) -1 and (a) -3, even when there is an inclination of 45 ° to 135 °, the upper left vertical edge and the lower left vertical edge are extracted.

  Similarly, as shown in FIG. 5B-2, the right edge of the vertical line has an upper right vertical edge when the right edge (falling edge) of the vertical line is in the upper right area and the right edge of the vertical line in the lower right area. When there is a falling edge), it is extracted as a lower right vertical edge, and when it has an inclination of 45 ° to 135 °, it is also extracted (FIGS. 5B-1 and 5B-3).

  As shown in FIG. 5C-2, the reference region is divided into four equal parts with the target pixel as the center. When the upper left region has an upper edge (rising edge) of the horizontal line, the upper left horizontal edge, and the upper right region has an upper edge of the horizontal line ( When there is a rising edge), it is extracted as the upper right horizontal edge. As shown in FIGS. 5 (c) -1 and (c) -3, even when there is an inclination of −45 ° to 45 °, the upper left horizontal edge and the upper right horizontal edge are extracted.

  Similarly, as shown in FIG. 5D, the lower edge of the horizontal line has a lower left horizontal edge when the lower left edge (falling edge) exists in the lower left area, and the lower edge of the horizontal line (lower edge) When there is a falling edge), it is extracted as a lower right lateral edge, and when it has an inclination of −45 ° to 45 °, it is also extracted (FIG. 5 (d) -1, (d) -3). In FIGS. 5A to 5D, a relatively thin line having a pair of edges in the reference region is illustrated as an example. However, as illustrated in FIG. Similarly, the upper left vertical edge and the lower left vertical edge are extracted for the edge of the thick line that does not exist in the reference area. Further, not only a straight line but also an upper left vertical edge and a lower left vertical edge including a smooth curved edge are extracted as shown in FIG. 5 (e) -2.

  FIG. 6A is a diagram for explaining processing of the horizontal pixel number counting unit 245 and the horizontal pixel number counting unit 246. A case where the pattern matching result of 13 pixels in the horizontal direction across the target pixel is (1) will be described as an example. The pattern matching result is multiplied by (2) the weight for counting the number of horizontal pixels corresponding to the pixel position. The weight is zero at the target pixel position, and the absolute value increases as the distance increases. The left direction is negative and the right direction is positive. That is, the result of (1) × (2) is obtained by determining how many pixels away from the pixel of interest the pixel matching the pattern is located in the left or right direction. The position of the pixel closest to the target pixel among the pixels matched with the pattern (select the one with the smallest absolute value from the result of (1) × (2)) is output as the result of the horizontal pixel count. However, (1) if the pattern matching result is all zero in the reference area, the pixel count result is the maximum value “7”.

  FIG. 6B is a diagram for explaining the processing of the vertical pixel number counting unit 247 and the vertical pixel number counting unit 248. A case where the pattern matching result of 13 pixels in the vertical direction across the target pixel is (1) will be described as an example. The pattern matching result is multiplied by (2) a weight for counting the number of vertical pixels corresponding to the pixel position. The weight is zero at the target pixel position, and the absolute value increases as the distance increases. The upward direction is negative and the downward direction is positive. That is, the result of (1) × (2) is obtained by determining how many pixels away from the pixel of interest the pixel matching the pattern is in the vertical direction. The position of the pixel closest to the target pixel among the pixels matched with the pattern (select the one having the smallest absolute value from the result of (1) × (2)) is output as the result of counting the number of vertical pixels. However, (1) if the pattern matching result is all zero in the reference area, the pixel count result is the maximum value “7”.

  FIG. 7A is a diagram for explaining reference regions and extraction conditions of the upper left vertical edge extraction unit 249 and the upper right vertical edge extraction unit 251. The horizontal pixel count results y1 to y7 are referred to the top seven pixels including the target pixel (y7) in the vertical direction, and (1) vertical line extraction conditions and (2) diagonal line extraction conditions (including smooth curves) ) Is output as the upper left vertical edge or the upper right vertical edge, and “0” is output when neither is satisfied. The first condition for both (1) vertical line extraction condition and (2) diagonal line extraction condition is that the result of the horizontal pixel count of all seven pixels is smaller than “7”, that is, the horizontal direction is always That is, there is a pixel matched by pattern matching. (1) The second condition of the vertical line extraction condition is that the difference between the maximum value and the minimum value of the horizontal pixel count is 1 or less. (2) The second condition of the diagonal line extraction condition is that the horizontal pixel count results are arranged in descending order from top to bottom. (1) The vertical line extraction condition is not only a vertical line with no noise, but also one dot on the vertical line as in the example of the character image on the halftone dot in FIG. Even if it is attached, it is a condition to extract as a vertical line, and the judgment condition is slightly looser than that of the diagonal line.

  FIG. 7B is a diagram for explaining reference regions and extraction conditions of the lower left vertical edge extraction unit 250 and the lower right vertical edge extraction unit 252. The results of the horizontal pixel count y7 to y13 are referred to the lower seven pixels including the target pixel (y7) in the vertical direction. The rest is the same as the upper left vertical edge extracting unit 249 and the upper right vertical edge extracting unit 251 of FIG.

  FIG. 8A is a diagram for explaining reference regions and extraction conditions of the upper left horizontal edge extraction unit 253 and the lower left horizontal edge extraction unit 255. The left pixel including the target pixel (y7) in the horizontal direction is referred to the results t1 to t7 of the vertical pixel count, and (1) horizontal line extraction condition, (2) diagonal line extraction condition (including a smooth curve) If either of these is satisfied, “1” is output as the upper left horizontal edge or the lower left horizontal edge, and “0” is output if neither is satisfied. The first condition for both (1) the horizontal line extraction condition and (2) the diagonal line extraction condition is that the result of counting the number of vertical pixels in all seven pixels is smaller than “7”, that is, the pattern is always in the vertical direction. That is, there is a pixel matched by matching. (1) The second condition of the horizontal line extraction condition is that the difference between the maximum value and the minimum value of the vertical direction pixel count is 1 or less. (2) The second condition of the diagonal line extraction condition is that the results of the vertical pixel count are arranged in descending order from left to right. (1) The horizontal line extraction condition is not only a completely noise-free horizontal line, but also a case where a horizontal line has a dot in the shape of a protrusion, so that it is extracted as a vertical line. The judgment conditions are slightly relaxed.

  FIG. 8B is a diagram for explaining reference regions and extraction conditions of the upper right horizontal edge extracting unit 254 and the lower right horizontal edge extracting unit 256. As a result of counting the number of pixels in the vertical direction, the right seven pixels including the target pixel (t7) are referred to in the horizontal direction. The rest is the same as the upper left horizontal edge extraction unit 253 and the lower left horizontal edge extraction unit 255 of FIG.

  Of the above-described extraction conditions for each edge, the one described as the second condition corresponds to the condition for determining the smoothness of the edge, and determines the presence or absence of local unevenness in the reference region.

  FIG. 9 shows the configuration of the continuous edge detection means 204, 214, 224. As shown in FIG. 9, it includes AND operations (AND) 261 to 264 and an OR operation (OR) 265. For example, as shown in FIG. 12A, when the pixel of interest is an upper left vertical edge and a lower left vertical edge, it is detected as a continuous edge. When smooth lines are continuous, continuous edges are detected, and mainly the edges of thick characters and lines are detected.

  FIG. 10 shows the configuration of the pair edge detection means 205, 215, and 225. As shown in FIG. 10, it is composed of logical product operations (AND) 271 to 274 and logical sum operation (OR) 275. For example, as shown in FIG. 12B, when the target pixel is an upper left vertical edge and an upper right vertical edge, or a lower left vertical edge and a lower right vertical edge, it is detected as a pair edge. Thin characters and lines are mainly detected. Further, as shown in FIG. 12C, edges near the end points are also detected as pair edges.

  FIG. 11 shows the configuration of the intersection edge detection means 206, 216, 226. As shown in FIG. 11, it is composed of logical product operations (AND) 281 to 288 and logical sum operation (OR) 289. For example, as shown in FIG. 12D, when the pixel of interest is an upper right horizontal edge and a lower left vertical edge, it is detected as an intersection edge. The intersections between the lines constituting the characters, the end points of the bold characters, and the corners of the broken lines are mainly detected.

  FIG. 13 shows the configuration of the character / picture identification means 207, 217, 227. A bold character (thick line) edge and a thin character (thin line) edge are identified, respectively. In the extraction of the bold character edge, correction is performed by the correction unit 291 and the result is expanded by the expansion unit 292. For a bold edge, a continuous edge and an intersection edge should be extracted from continuous edges / pair edges / intersection edges. The correcting means 291 outputs “1” if a continuous edge or intersection edge is detected in all the pixels in the 5 × 5 reference region, and “0” if there is a pixel that is neither a continuous edge nor an intersection edge. Is output. The expansion unit 292 refers to the result of the 9 × 9 pixel correction unit 291 and extracts “1” as a bold character edge. The extraction of the fine character edge is corrected by the correction unit 293 and the result is expanded by the expansion unit 294. In the fine character edge, the pair edge and the intersection edge should be extracted from the continuous edge / pair edge / intersection edge. The correction unit 293 outputs “1” if a pair edge or intersection edge is detected in all the pixels in the 5 × 5 reference region, and outputs “0” if a pixel that is neither a pair edge nor an intersection edge exists. . The expansion unit 294 refers to the result of the 9 × 9 pixel correction unit 293, and extracts “1” as a fine character edge.

The processing from edge smoothness determination to character picture identification will be described with two specific examples of the case of a character edge on a halftone dot and the case of a middle edge of a halftone dot pattern.
(Specific example 1 of edge smoothness determination)
FIG. 14 is an image after binarization of a character edge on a halftone dot. FIG. 15 shows the result of the P1 pattern matching 241 for the image of FIG. 14 and the result of the horizontal pixel count counting unit 245. Since both the upper left vertical edge and the lower left vertical edge have the horizontal pixel count result = 7, The extraction result is “0”.

  FIG. 16 shows the result of the P2 pattern matching 242 for the image of FIG. 14 and the result of the horizontal pixel count counting means 246. Since the upper right vertical edge and the lower right vertical edge both have the horizontal pixel count result = 7. The extraction result is “0”.

  FIG. 17 shows the result of the P3 pattern matching 243 for the image of FIG. 14 and the result of the vertical pixel count counting means 247. The vertical pixel count results are all less than 7 for both the upper left horizontal edge and the upper right horizontal edge, and Since they are arranged in descending order from left to right, the extraction result is “1”.

  FIG. 18 shows the result of P4 pattern matching 244 for the image of FIG. 14 and the result of the vertical pixel count counting means 248. Both the lower left horizontal edge and the lower right horizontal edge have vertical pixel count results of less than 7, and Since they are arranged in descending order from left to right, the extraction result is “1”.

As a result of the edge smoothness determination for the image of FIG. 14 from the above extraction results, what is extracted as a smooth edge is the upper left horizontal edge, the upper right horizontal edge, the lower left horizontal edge, and the lower right horizontal edge. It will be. This is detected as a continuous edge and a pair edge, and finally identified as a character edge (unless excluded by the correction means 293).
(Specific example 2 of edge smoothness determination)
FIG. 19 shows an image after binarization of the halftone dot pattern. FIG. 20 shows the result of the P1 pattern matching 241 and the result of the horizontal pixel number counting unit 245 for the image of FIG. In the upper left vertical edge, the horizontal pixel count results are all less than 7 and are arranged in descending order from top to bottom, so the extraction result is “1”, and the lower left vertical edge is the horizontal pixel count result = 7. Therefore, the extraction result is “0”.

  FIG. 21 shows the result of the P2 pattern matching 242 and the result of the horizontal pixel count counting unit 246 for the image of FIG. 19. The upper right vertical edge has the horizontal pixel count result = 7, and the extraction result is “0”. The lower right vertical edge has all horizontal pixel count results of less than 7 and is arranged in descending order from top to bottom, so the extraction result is “1”.

  FIG. 22 shows the result of the P3 pattern matching 243 for the image of FIG. 19 and the result of the vertical pixel count counting means 247. The upper left horizontal edge shows that the vertical pixel count results are all less than 7, but from left to right. Since the pixels are not arranged in descending order, the extraction result is “0”. In the upper right horizontal edge, all the vertical pixel count results are less than 7 and are arranged in descending order from left to right. "become.

  FIG. 23 shows the result of the P4 pattern matching 244 for the image of FIG. 19 and the result of the vertical pixel count counting means 248. The lower left horizontal edge indicates that the vertical pixel count results are all less than 7 and from left to right. Since the pixels are arranged in descending order, the extraction result is “1”, and the lower right horizontal edge has all the vertical pixel count results less than 7, but the extraction result is “from left to right. 0 ”.

  As a result of the edge smoothness determination for the image in FIG. 19, it is said that the upper left vertical edge, the lower right vertical edge, the upper right horizontal edge, and the lower left horizontal edge are extracted as smooth edges. It will be. This is not a continuous edge, a pair edge, or an intersection edge, and the final result is not a character edge. It should be noted that the upper left horizontal edge and the lower right horizontal edge are judged as non-smooth edges due to local unevenness in the reference area, and are not extracted. It prevents that.

  FIG. 24 shows the configuration of the edge amount extraction means 30. The signal synthesis unit 301 synthesizes the R′G′B ′ signal ((R ′ + 2G ′ + B ′) / 4) and converts it into one signal, and the edge amount detection unit 302 detects the edge amount.

  FIG. 25A shows the configuration of the edge amount detection means 302. In the edge amount detection filters 303 to 306 (1 to 4), masking calculation is performed using four types of 7 × 7 filters shown in FIGS. Of the four outputs, the one with the maximum absolute value is selected by the maximum value selection means 311 and output.

  FIG. 26 shows the configuration of the filter processing means 13. The smoothing means 131 and the edge emphasis means 132 are subjected to filter processing, and the result of the two filter processes is synthesized at a ratio corresponding to the edge amount and the character edge. In the case of a character edge, according to the edge amount, smoothing result when the edge amount is maximum: edge enhancement result is 1:10, and when the edge amount is minimum, 1: 0 (only the smoothing result is valid), edge amount When is an intermediate value, it is synthesized at an intermediate ratio. When it is not a character edge, it is always combined at a ratio of 1: 0. It is effective for emphasizing the white ground character edge and the halftone dot character edge to reproduce sharply, and to reproduce the picture portion smoothly with good graininess.

  FIG. 27 shows the configuration of the edge amount extraction means 40. For each color C′M′Y ′, the edge amount detection means 401, 402, and 403 detect multivalued edge amounts. The configuration of the edge amount detection means is the same as that of the edge amount extraction means 30 (FIG. 25A). However, the edge amount detection filter of 5 × 5 size shown in FIGS. 27B to 27E is used. Since the edge amount is detected from the signal after the filter processing, the size of the detection filter is changed in consideration of the difference in resolution. The maximum value selection unit 404 selects the maximum one of the detected edge amounts of the three colors. On the other hand, the signal synthesizing unit 405 adds the three signals C′M′Y ′ at a predetermined ratio and converts them into one signal. For example, (C ′ × 1/4 + M ′ × 2/4 + Y ′ × 1/4) is output as a synthesized signal. Laplacian filter operation means 406 performs masking operation on the synthesized signal using the filter of FIG. 27 (f), and sign determination means 407 determines whether or not it is a positive value. 1 if positive, 0 if negative. The inner edge amount extraction unit 408 outputs the output value of the maximum value selection unit 404 as it is when the result of the sign determination unit 407 is “1”. If the result of the sign determination means 407 is “0”, the edge amount is canceled and 0 is output. The Laplacian filter calculation means 406 outputs a positive value on the high density side of the edge corresponding to the inner edge of the character and a negative value on the low density side of the edge corresponding to the outer edge of the character. The edge amount is valid only at the inner edge where the Laplacian is positive, and the edge amount is invalid at the outer edge where the Laplacian is negative. The N-value converting unit 409 quantizes the N-value. N is determined according to the number of stages in which processing is controlled in the UCR / black generation means 15 to which the edge amount extraction result is applied, but here it is controlled in two stages in order to simplify the explanation. It is assumed that the inner edge amount extracted with a value of 0 to 63 is quantized to 0/1 (N = 2).

The UCR / black generation means 15 includes a method of performing both black generation and UCR using an equation, a method using an LUT, and the like. In this embodiment, a case where black generation is performed using an LUT and UCR is performed using an equation will be described. FIG. 28 shows a black generation LUT. In black generation, the minimum value Min (C ′, M ′, Y ′) of C ′, M ′, Y ′ (0 is white, 255 is black) is calculated and input to the LUT to obtain an output value. . UCR is performed by the following equation. α is a UCR adjustment parameter.
C = C′−α × K
M = C′−α × K
Y = C′−α × K (Formula 4)
When the output value of the character edge and edge amount extraction means 40 is “1”, the black generation table-1 is used, Min (C, M′Y ′) is replaced with K as it is, and the UCR adjustment parameter is α = 1. To do. In other cases, the black generation table-2 is used, and K is not generated in the highlight, but K is gradually generated from the middle. The UCR adjustment parameter is α = 0.5. As in the filter processing, the edge amount may be obtained in multiple values, and a number of intermediate black generation tables may be prepared and controlled in multiple stages. By switching between black ink generation and UCR in this way, it is possible to reproduce characters (black characters) sharply without coloring and to reproduce a pattern with high gradation.

  As described above, according to the present embodiment, by focusing on the local unevenness of the edge and determining whether or not the edge is a smooth edge, the character edge on the halftone dot is discriminated from the edge in the halftone dot pattern. Can do. Furthermore, by applying the identification result to filter processing and black ink processing, it is possible to achieve both high-quality reproduction of a halftone dot character edge and a halftone dot pattern. In this embodiment, an example in which the character / pattern identification result is applied to the filter processing and the black processing is shown. However, the conventional technology uses the character / pattern identification result for various image quality enhancement processing such as color correction and pseudo gradation processing. Of course, it is also effective to apply the identification result of the present invention to them.

Example 2:
In Example 1, the example which applies the character pattern identification result of this invention to an image quality improvement process was shown. In the second embodiment, an example is shown in which the present invention is applied to a toner save process that is not an image quality improvement process but an image quality processing. The toner saving process is a process for suppressing the toner consumption amount by lowering the density and outputting. The “toner save” toner is not limited to powder toner. This includes liquid toner (ink).

  FIG. 29 shows the configuration of the second embodiment of the present invention. On the operation panel 50, when the user wants to output in the toner save mode, the toner save mode can be selected and designated. When the user designates the toner save mode, the toner save processing means 19 performs toner save processing.

  The toner save processing unit 19 performs toner save processing according to the signal from the operation panel 50, the result of the image attribute determination unit 20 (whether it is a character edge), and the output value of the edge amount extraction unit 40. Switch.

  The toner saving process is performed by the γ conversion of FIG. When toner save γ-1 is used, the input value = the output value, the density is stored as it is, and the toner save process is not substantially performed. When toner save γ-2 is used, processing is performed in which the output value is about 50% of the input value. When the toner save mode is not designated on the operation panel 50, the toner save γ-1 is always used. When the toner save mode is designated on the operation panel 50, if it is a character edge and the output value of edge amount extraction is “1”, toner save processing is performed using toner save γ−1 ( Practically not). In other cases, toner save γ-2 is used.

  As a result, when the toner save mode is set, the density of the white background character edge and the character edge on the halftone dot is preserved to maintain the character legibility, while effectively saving the toner on the picture portion, and Generates a good toner-save image even in the picture part, suppressing image quality defects (faults that frequently occur when the accuracy of character / pattern identification is poor) where the edges of the dot pattern are conserved in density like character edges be able to.

  As described above, according to the second embodiment, by applying the character / pattern identification according to the first embodiment to the toner saving process, it is possible to simultaneously maintain the character readability, suppress the defect in the image quality, and exhibit the toner saving effect. Can do. Thus, the identification technique of the present invention is not limited to image processing for improving image quality, and the applicable range is extremely wide. The toner save mentioned here is one of the application examples. In addition, for example, it can be effectively applied to a technique for increasing the compression efficiency by identifying a character pattern and changing the compression method.

  The present invention also supplies a storage medium storing software program codes for realizing the processing procedures and functions of the above-described embodiments to a system or apparatus, and a computer (CPU or MPU) of the system or apparatus stores the storage medium. This can also be achieved by reading and executing the program code stored in. In this case, the program code itself read from the storage medium realizes the processing procedures and functions of the above-described embodiment. As a storage medium for supplying the program code, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used. Further, by executing the program code read by the computer, not only the processing procedures and functions of the above-described embodiments are realized, but also an OS (operating system) running on the computer based on the instruction of the program code. ) Etc. perform part or all of the actual processing, and the processing procedures and functions of the above-described embodiments are realized by the processing. Further, after the program code read from the storage medium is written into a memory provided in a function expansion board inserted into the computer or a function expansion unit connected to the computer, the function expansion is performed based on the instruction of the program code. This includes the case where the CPU or the like provided in the board or the function expansion unit performs part or all of the actual processing, and the processing procedures and functions of the above-described embodiments are realized by the processing.

The structure of Example 1 of this invention is shown. The structure of an image attribute determination means is shown. The structure of a binarization means is shown. The structure of the smoothness determination means of an edge is shown. An example of an edge to be extracted is shown. It is a figure explaining the process of a horizontal / vertical direction pixel number count means. It is a figure explaining the reference area and extraction conditions of an upper left / upper right vertical edge extraction means and a lower left / lower right vertical edge extraction means. It is a figure explaining the reference area and extraction conditions of an upper left / lower left horizontal edge extraction means and an upper right / lower right horizontal edge extraction means. The structure of a continuous edge detection means is shown. The structure of a pair edge detection means is shown. The structure of an intersection edge detection means is shown. It is a figure explaining the detection of a continuous edge, a pair edge, and an intersection edge. The structure of a character pattern identification means is shown. An image after binarization of a character edge on a halftone dot is shown. The result of P1 pattern matching with respect to the image of FIG. 14 and the result of a horizontal direction pixel number count means are shown. The result of the P2 pattern matching with respect to the image of FIG. 14 and the result of the horizontal pixel number counting means are shown. The result of the P3 pattern matching with respect to the image of FIG. 14 and the result of a vertical direction pixel number count means are shown. The result of the P4 pattern matching with respect to the image of FIG. 14 and the result of a vertical direction pixel number count means are shown. An image after binarization of an edge in a halftone dot pattern is shown. The result of P1 pattern matching with respect to the image of FIG. 19 and the result of the horizontal pixel number counting means are shown. The result of the P2 pattern matching with respect to the image of FIG. 19 and the result of the horizontal pixel number counting means are shown. The result of P3 pattern matching with respect to the image of FIG. 19 and the result of the number of pixels in the vertical direction are shown. The result of P4 pattern matching with respect to the image of FIG. 19 and the result of the number of pixels in the vertical direction are shown. The structure of an edge amount extraction means is shown. The structure of an edge amount detection means is shown. The structure of a filter process means is shown. The structure of an edge amount extraction means is shown. A black generation LUT is shown. The structure of Example 2 of this invention is shown. FIG. 10 is a diagram illustrating toner save processing. It is a figure explaining the conventional subject.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Scanner 11 Scanner gamma correction means 12 Scanner color correction means 13 Filter processing means 14 Printer color correction means 15 UCR / Black processing means 16 Printer gamma correction means 17 Pseudo halftone processing means 18 Printer 20 Image attribute judgment means 30, 40 Edge amount Extraction means

Claims (5)

When the difference between the average value of two pixel values at symmetrical positions across the center pixel of the n pixels × n pixel region constituting the image and the pixel value of the center pixel is larger than a predetermined threshold, Peak pixel detection means for detecting a central pixel as a peak pixel, setting means for setting a threshold based on the pixel value of the detected peak pixel, and binarization means for binarizing the image using the threshold Extraction means for extracting an upper left vertical edge, a lower left vertical edge, an upper right vertical edge, a lower right vertical edge, an upper left horizontal edge, an upper right horizontal edge, a lower left horizontal edge, and a lower right horizontal edge from the binarized image; If the extracted edge is an upper left vertical edge and a lower left vertical edge, it is detected as a continuous edge, and if the extracted edge is an upper left vertical edge and an upper right vertical edge, or a lower left vertical edge and a lower right vertical edge, Peyer Detected as di, when the extracted edge of the upper right lateral edge and the lower left vertical edge, the intersection edge detecting means for detecting as an edge detection result to the character edge to character edge on halftone dots, based of the edge detecting means An image processing apparatus having an identification means for identifying as follows. The image processing apparatus according to claim 1, wherein the setting unit sets a value obtained by adding a predetermined value to the maximum value of the detected peak pixel as the threshold value. When the difference between the average value of two pixel values at symmetrical positions across the center pixel of the n pixels × n pixel region constituting the image and the pixel value of the center pixel is larger than a predetermined threshold, A peak pixel detecting step for detecting a central pixel as a peak pixel, a setting step for setting a threshold based on the pixel value of the detected peak pixel, and a binarization step for binarizing the image using the threshold An extraction step of extracting an upper left vertical edge, a lower left vertical edge, an upper right vertical edge, a lower right vertical edge, an upper left horizontal edge, an upper right horizontal edge, a lower left horizontal edge, and a lower right horizontal edge from the binarized image; If the extracted edge is an upper left vertical edge and a lower left vertical edge, it is detected as a continuous edge, and if the extracted edge is an upper left vertical edge and an upper right vertical edge, or a lower left vertical edge and a lower right vertical edge, Peyer Detected as di, when the extracted edge of the upper right lateral edge and the lower left vertical edge, and an edge detection step of detecting as the intersection edge, the edge detection result of detection to the character edge character edge on halftone dots based process An image processing method comprising an identification step of identifying as A program for causing a computer to realize the image processing method according to claim 3 . A computer-readable recording medium recording a program for causing a computer to implement the image processing method according to claim 3 .

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