Image processing apparatus

Abstract: There is provided an image processing apparatus comprising: an input device to input an image signal; a processor for processing the input image signal and outputting an image reproduction signal; a detecting circuit to detect characteristics of the input image signal; and a control circuit to control processing conditions of the image signal by the processor by a fuzzy inference based on the characteristics of the image signal which were detected by the detecting circuit. The characteristics of the image signal include the kinds of images such as character, screen, and photograph. The optimum spatial filtering process can be executed for each of the character, screen, and photograph areas. A mixing ratio of the edge emphasis and the smoothing can be continuously changed for characteristic amounts of an original. Membership functions of a plurality of parameters are synthesized and a consequence is derived by the fuzzy inference algorithm. Thus, erroneous judgment for a vague image area is prevented and discontinuity which occurs at a change point of the image processes can be also eliminated.

Description: BACKGROUND OF THE INVENTION The present invention relates to an image processing apparatus for executing a predetermined process to an input image and for outputting the processed image. Hitherto, there exists an apparatus for executing image processes such that a spatial frequency filtering process is digitally executed to input image data and spatial frequency characteristics are changed and the processed data is output. As such an apparatus, a technique in which an input image is separated into three kinds of character original, screen (or dot) original, and photograph original and different processes are executed for them has been shown in US-A-4,194,221. On the other hand, a technique in which an edge emphasis process and smoothing filtering process are switched in accordance with an original has been proposed in US-A-4,953,114 by the same assignee as the assignee of the present invention. However, according to the above conventional techniques, for instance, in the case where the kind of input image was erroneously discriminated, there is a fear such that a character of an output image becomes extremely dull, the móire of screen is emphasized, or the like, so that the picture quality is remarkably deteriorated. On the other hand, there is a case where in a portion where the kind of image changes, the picture quality suddenly changes, so that the output image cannot be seen well. Generally, in a color image copying apparatus, color separation signals of R (red), G (green), and B (blue) are input from an image input device and are subjected to process such as luminance (light amount) - density conversion, masking, and the like, and a processed image is printed and recorded by an image output device by an ink jet method or an electrophotographic method. In the masking process, color signals of Y (yellow), M (magenta), C (cyan), and K (black) are obtained by matrix calculations from density signals D r , D g , and D b after the density conversion. However, ordinarily, the optimization of masking coefficients a ij , that is, the non-linear masking is executed by a method of least square or the like so as to minimize the color differences within a color reproduction range by using high-order terms such as D r D g , D g D b , D b D r , D r 2 , D g 2 , D b 2 , and the like as well as D r , D g , and D b in consideration of the non-linearity or the like of the image reproducing characteristics of the printer. On the other hand, since the discriminating capability of human being for the achromatic color, skin color, or the like is high, an advanced color reproducibility is required for a copy image with regard to an original including those colors. However, according to the above conventional technique, in order to raise the reproducibility of the achromatic color, skin color, or the like and to realize a sufficient color reproducibility in the whole color reproduction range, the masking process including the terms of higher order must be executed. Thus, there are drawbacks such that the masking circuit becomes complicated and a method of determining the optimum coefficients becomes troublesome. On the other hand, there are circumstances such that there is an individual difference of the degree when the color such as achromatic color, skin color, or the like is perceived by the visual sense of human being and such a degree is dominated by vague parameters based on the subjectivity to a certain extent. The present invention is made in consideration of the foregoing circumstances and has as a concern the provision of an image processing apparatus which can eliminate the drawbacks of the conventional techniques as mentioned above. US-A-4783840 discloses image enhancing apparatus in which a single adaptive filter has its parameters varied in accordance with detected patterns of the image. In accordance with the present invention there is provided image processing apparatus as set out in claim 1. The above and other features of the present invention will become apparent from the following detailed description and the appended claims with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS Figs. 1A to 1D are block diagrams showing examples of a construction of the present invention; Figs. 2A to 2C are diagrams showing examples of a spatial filter; Figs. 3A to 3J are diagrams showing examples of membership functions which are used in a judgement circuit; Fig. 4 is a block diagram showing a whole construction of the invention; Figs. 5A to 5C are diagrams showing other examples of a spatial filter; Fig. 6 is a diagram showing examples of membership functions corresponding to those in Fig. 5; Figs. 7A to 7C are diagrams showing membership functions and rules in the third embodiment of the invention; Fig. 8 is a whole block diagram of the third embodiment of the invention; Figs. 9A to 9C are block diagrams and the like of a color conversion circuit 1; Fig. 10 is a diagram showing a color distribution in an I-Q area; and Figs. 11A to 11E and 12 are diagrams for explaining examples of the fuzzy inference. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will be described hereinbelow with reference to the drawings. <Embodiment 1> Fig. 4 shows an example of a construction of an image processing apparatus of the present invention. An original 402 put on an original support glass 401 is irradiated by an irradiating halogen lamp 403 and is scanned in the direction of an arrow. The scanned image is formed onto a CCD line sensor 405 by an image forming lens 404 and is converted into an image signal every line. An output of the CCD is converted into a digital signal every pixel by a sample and hold (S/H) circuit 406 and an A/D conversion circuit 407 and becomes an output of, for instance, eight bits with respect to one pixel. An output of the A/D conversion circuit (A/D converter) 407 is subjected to spatial filtering processes such as smoothing, edge emphasis, and the like by a line buffer 408 and a filter circuit 409 and is properly gamma corrected by a gamma conversion circuit 410. After that, the signal is sent to a D/A conversion circuit 411 and is again converted into the analog signal. An output of the D/A conversion circuit 411 is compared with an analog triangle wave of one pixel period which is generated from a triangle wave generation circuit 412 by a comparator 413. An output of the comparator 413 is input to a laser driver 414. A laser 415 is driven by the laser driver 414 by a pulse width corresponding to a density level of every pixel. A laser beam emitted from the laser 415 is irradiated to a polygonal mirror 416 which rotates at a high speed, so that an image is written onto a photo sensitive drum 417. An electrostatic latent image on the photo sensitive drum is developed by a developing device (not shown) and is fixed by a fixing device (not shown) so that a copy image is output. Original images in such a kind of image copying apparatus are mainly classified to the following three kinds of originals: a character original; an original such as a silver salt photograph having continuous gradations; and an original such as a printed matter which is constructed by a micro screen structure. When considering output images for the above various kinds of originals, the sharpness of an output image in the case of a character original is lost as compared with that of the inherent original of the input image due to influences by the spatial frequency characteristics of the image forming lens system and the CCD. It is desirable to execute a certain kind of edge emphasis by the filter circuit 409. In the case of a screen original, the sampling period of the CCD and the period of the screen cause a beat and an unpleasant móire pattern occurs. Therefore, it is desirable to execute a smoothing process by the spatial filter circuit 409. On the other hand, in the case of a photograph original, it corresponds to an intermediate image between the character image and the screen image. If the smoothing process is executed, a photograph image becomes dull as a whole. If the edge emphasis is executed, a photograph image becomes a noisy image. Therefore, it is preferable not to execute the smoothing and edge emphasis for a photograph original. To eliminate the above drawbacks, for instance, there is considered a method of switching between the edge emphasis and the smoothing in accordance with an edge amount of an input original. However, there is a problem such that unnaturality of the image occurs in the switching portion between the edge emphasis and the smoothing. On the other hand, there is also considered a method whereby a weighted mean of outputs of two spatial filters to execute the edge emphasis and the smoothing is calculated at a ratio according to a feature of an input original. However, the process for a photograph original as an intermediate original between the screen image and the character image is not always clear. Further, in both of the above methods, there is a problem such that in the case where the character portion was determined to be a screen area or, contrarily, the screen portion was decided to be a character area, the image deteriorates such that the character becomes extremely dull or the móire of the screen is emphasized. Therefore, in the embodiment, there are provided two kinds of spatial filter circuits in which window sizes for the edge emphasis and the smoothing are equal, and coefficient values are set in a manner such that when both of outputs of the two spatial filter circuits are added at a predetermined ratio, all of filter coefficients of the pixels other than the central pixel are set to 0. With such set values, the image tone of original image is identified, a weighted mean of the output of the edge emphasizing means and the output of the smoothing means is calculated at a proper ratio on the basis of the discrimination results, and in the case where the original is determined to be a screen image (or dot image), the ratio for the smoothing is raised. In the case where the original is decided to be a character image, the ratio for the edge emphasis is increased. In the case where the original is determined to be a photograph image, the ratio is set such that all of the filter coefficients of the pixels other than the central pixel are set to 0. If the result of the discrimination indicates an intermediate image among the above three images, the ratio is set to an intermediate value among the above three ratios. In this manner, the deterioration of image due to an erroneous discrimination is prevented. A construction of the image processing apparatus of the invention will now be described in detail hereinbelow with reference to Figs. 1A to 1D. In Fig. 1A, the CCD 405, sample and hold circuit 406, A/D converter (A/D conversion circuit) 407, and gamma conversion circuit 410 are similar to those in Fig. 4 and their descriptions are omitted. The apparatus shown in Fig. 1A also has a construction similar to that shown in Fig. 4 other than the above components. In Fig. 1A, reference numeral 101 denotes a line buffer (memory) of a plurality of lines to delay the image signal of every pixel in order to compensate the delay of the timing due to the discrimination of the image tone discriminating circuit 106 to 108; 102 and 103 a line buffer and a smoothing circuit to execute the smoothing of an input image; 104 and 105 a line buffer and an edge emphasis circuit to execute the edge emphasis; 106 a character detection circuit; 107 a screen detection circuit; and 108 a judgement circuit. The circuits 106, 107, and 108 are used to determine that a target area of the input original is close to which one of the character, screen, and photograph images and output a weighted mean ratio α between the output after the smoothing process and the output after the edge emphasizing process as a result of the discrimination. The ratio α is multiplied to the smoothing output by a multiplier 109. The value of (1 - α) which was calculated by a subtracter 111 is multiplied to the edge emphasis output by a multiplier 110. Reference numeral 112 denotes an adder for adding outputs of the multipliers 109 and 110 and obtaining the weighted mean value. An output of the adder 112 is gamma converted by the gamma conversion circuit 410. After that, a signal of the gamma conversion circuit 410 is output as a copy image in a manner similar to Fig. 4. The weighted mean ratio α will now be described. It is now assumed that a smoothing filter is as shown in Fig. 2A and an edge emphasis filter is as shown in Fig. 2B and each of the filters has a window size of 3 x 3. Assuming that an output of the smoothing circuit 103 is set to S and an output of the edge emphasis circuit 105 is set to E, the obtaining of the weighted mean of both of the outputs S and E denotes that Y is obtained by the following calculation (1). y = αS + (1 - α)E where, α is a real number between 0 and 1. The above process will now be considered every pixel before filtering. In the filters of Figs. 2A and 2B, such a process is equivalent to that the calculation of y 0 = 1 9 α + 9 (1 - α) is executed for the central pixel and that the calculation of Y 1 = 1 9 α - (1 - α) is executed for the peripheral pixels around the central pixel. Now, assuming that α = 9/10, y 0 = 1 from the equation (2) and y 1 = 0 from the equation (3). In the case where the weighted mean ratio α is set to 9/10, the equation (1) eventually corresponds to a filter of Fig. 2C, that is, it is equivalent to that no process is performed for the input image. It will obviously be understood that the case of α = 1 corresponds to the case of only the smoothing process and that the case of α = 0 corresponds to the case of only the edge emphasizing process. Therefore, it is sufficient to construct the image tone discriminating circuit 106 to 108 in a manner such that 1 is output as α if the original is determined to be a screen area, 0 is output as α if the original is decided as a character area, and 9/10 is output as α if the original is determined to be a photograph area. Further, the invention is characterized in that the value of α is almost continuously changed from 0 to 1 without completely limiting to only the three kinds of values as mentioned above, thereby also eliminating the unnaturality and discontinuity which appear on the output image at a switching point of α and also preventing the image deterioration due to the erroneous judgement. The discriminating circuit 106 to 108 to continuously execute the judgement as mentioned above will now be described. Reference numerals 106 and 107 denote the well-known character detection circuit and the screen detection circuit. For instance, an edge detection circuit in which a target pixel is set to the central pixel can be used as a character detection circuit. In this case, since edge amounts are large in the character area and screen area and an edge amount is small in the photograph area, the edge amount is output as a signal from the character detection circuit 106. Fig. 1B shows an example of the character detection circuit. Reference numerals 121 to 124 denote line memories each for delaying the image data of one line; 125 indicates a max detection circuit to detect the maximum value in a pixel block of 5 x 5 in the case where the target pixel is set to the central pixel; 126 a min detection circuit to detect the minimum value in a pixel block of 5 x 5 in the case where the target pixel is set to the central pixel; and 127 a subtraction circuit to calculate the difference between the maximum and minimum values. An output of the subtraction circuit 127 denotes the edge amount and corresponds to the output of the character detection circuit 106. As a screen detection circuit 107, it is possible to use a circuit to count the number of pixels which were changed from white to black or from black to white per unit area in the case where the pixels near the target pixel were binarized by a predetermined threshold value. In this case, in the screen area, since a number of micro dots exist, the count value is large. On the other hand, in the character and photograph areas, since the count numbers are small, such a count number can be used as an output of the screen detection circuit 107. However, even in the photograph area, if this area contains fine noise components, there is a possibility such that the count number is large. The screen detection circuit can be also constructed as shown in Fig. 1C. Reference numeral 128 denotes a binarization circuit to convert input multi-value image data into binary data; 129 to 132 indicate line memories; and 133 a change point detection circuit to detect the number of change points from 0 to 1 and from 1 to 0 with respect to the binary data in a pixel block of 5 x 5 in the case where the target pixel is set to the central pixel. The change point detection circuit 133 detects the number(s) of change points in either one of or both of the main scanning direction and the sub scanning direction. Reference numeral 134 indicates a dilator circuit. With respect to each pixel in the pixel block of 5 x 5, if any one pixel of the value of "1" exists in nine pixels including eight peripheral pixels, the values of all of the nine pixels are set to 1, thereby dilating the binary image. Reference numeral 135 denotes a change point detection circuit to detect the number of change points with respect to the dilated binary image in a manner similar to the change point detection circuit 133; 136 a delay circuit to match the timings for outputs of the change point detection circuits 133 and 135; and 137 a subtraction circuit to calculate the difference between the outputs of the detection circuits 133 and 135. In the case of the screen area, since the binary image changes at a relatively short period, if the image was dilated, a reduction amount of the change points increases. On the contrary, in the case of the character and photograph areas, the reduction amount of the change points is relatively small. It will be understood from the above description that in the screen area, both of the edge amount and the count number of changed pixels are large and in the character area, the edge amount increases and the count number is small. On the other hand, in the photograph area, both of the edge amount and the count number are small or the edge amount is small and the count number is large. That is, the following fuzzy rules are considered. 1 ○ If (the edge amount is large) and (the count value is large), then screen area 2 ○ If (the edge amount is large) and (the count value is small), then character area 3 ○ If (the edge amount is small) and (the count value is small), then photograph area 4 ○ If (the edge amount is small) and (the count value is large), then photograph area When the above relations are expressed by membership functions which are used in the fuzzy inference, they are as shown in Figs. 3A to 3F. Fig. 3A shows the membership functions for the edge amount, in which reference numeral 301 indicates "a degree at which the edge amount is small" and 302 indicates "a degree at which the edge amount is large". Fig. 3B shows the membership functions for the count value, in which reference numeral 303 indicates "a degree at which the count value is small" and 304 indicates "a degree at which the count value is large". Fig. 3C shows the membership functions indicative of the conclusion sections, in which reference numeral 305 indicates "a degree at which the image corresponds to a character area", 306 indicates "a degree at which the image corresponds to a photograph area", and 307 indicates "a degree at which the image corresponds to a screen area". The value of α is obtained by using the above rules regarding the edge amount and the count value. Now, assuming that the edge amount is set to e 1 and the count value is set to c 1 , a conformity with the conclusion indicating that "the image corresponds to a screen area" is first obtained by using the rule 1 ○. Since the screen area relates to the case where both of an edge amount e and a count value c are large, an intersection e ℓ1 of e 1 and 302 and an intersection c ℓ1 of c 1 and 304 are obtained. e ℓ1 and c ℓ1 . are compared and the conclusion section 307 is cut away by the smaller value e ℓ1 . Thus, the inside hatched portion of 307 shows a screen likelihood. In a manner similar to the above, a conformity with the conclusion indicating that "the image corresponds to a character area" is obtained by using the rule 2 ○. Since the character area relates to the case where the edge amount e is large and the count value c is small, the intersection e ℓ1 of e 1 and 302 and an intersection c m1 of c 1 and 303 are obtained. e ℓ1 and c m1 are compared and the conclusion section 305 is cut away by the smaller value c m1 . In a manner similar to the above, a photograph likelihood is obtained by using the rules 3 ○ and 4 ○ and their overlap portion is derived, so that the hatched region in Fig. 3C is obtained. A barycenter of the hatched region is obtained and the position of the barycenter on an axis of abscissa is obtained. This position is set to a value α 1 of α to be derived. In the case of this example, since both of the values of e 1 and c 1 are relatively large, the resultant value of α 1 is close to 1. On the other hand, as shown in Figs. 3D to 3F, assuming that the edge amount is set to e 2 and the count value is set to c 2 , α 2 is also similarly obtained. In this case, since e 2 is relatively large and c 2 is relatively small, the image is near a character area. Therefore, it will be understood that the value of α 2 is near 0. For the membership functions in Figs. 3A to 3F, for instance, each of the functions 301 to 307 shown in Figs. 3A to 3F is stored into an ROM or an RAM as a one-dimensional lookup table provided in the judgement circuit 108 and the calculation result α can be obtained from the output of each table. Fig. 3G shows a construction to realize such a method. Reference numerals 310, 311, and 312 denote a ROM(A), a ROM(B), and a ROM(C), respectively. The membership functions regarding the edge amount as one of the state amounts are stored in the ROM (A) 310. By inputting the edge amount e from the character detection circuit 106, conformities e m and e ℓ for the membership functions 301 and 302 are output from the ROM 310. Similarly, the membership functions regarding the count value as one of the state amounts are stored in the ROM (B) 311. By inputting the count value c from the screen detection circuit 107, conformities c m and c ℓ for the membership functions 303 and 304 are output from the ROM 311. The correspondences among the conformities e ℓ , e m , c ℓ and C m of the input signals e and c for the membership functions of the state amounts and α obtained by the above method are stored in the ROM (C) 312. That is, the ROM (C) 312 stores the results obtained by executing the comparing processes of the conformities e ℓ , e m , c ℓ , and c m of the input signals e and c for the membership functions of the state amounts, the calculating processes of the conformities with the conclusion sections based on the foregoing rules, the calculating process of the barycenter, and the calculating process of the control amount α, As shown in Fig. 3H, the judgement circuit 108 can be realized by a memory to store the values of α in a two-dimensional table format such as to output α for the input e and c. Such a memory can be constructed by a ROM as shown in Fig. 3H or RAM. On the other hand, an shown in Fig. 3I, the judgement circuit 108 can be also constructed by a software using a CPU. In Fig. 3I, reference numeral 314 denotes a memory to store the input state amounts e and c and the control amount α as a result of the calculation; 315 a CPU to execute arithmetic operations, which will be explained hereinafter; 316 a RAM which is mainly used as a work area; and 317 a ROM to store operation programs, membership functions, fuzzy rules, and the like. A processing flow will now be described with reference to a flowchart of Fig. 3J. First, the edge amount e and the count value c as state amounts are input to the memory 314 (step S350). The above state amounts can be input to the memory by an amount of one frame or an amount of a few lines in accordance with the operating speed of the CPU 315. Then, the conformities e ℓ , e m , c ℓ , and c m for each membership function of the edge amount e and count value c are calculated every pixel by using the membership functions stored in the ROM 317 (S351) and are temporarily stored into the RAM 316. The conformities e ℓ , e m , c ℓ , and c m are compared by the CPU 315 (S352). The conformity for the ith rule among the rules stored in the ROM 317 is calculated (S353) and is stored into the RAM 316. The above processes are repeated with respect to all of the rules (S354). A barycenter is calculated from all of the conformities (S355). The control amount α is output (S356). The operations of the fuzzy inference are completed as mentioned above. According to the above embodiment of the invention, the optimum spatial filtering process can be executed for each of the character area, photograph area, and screen area in the original image. On the other hand, since the mixing ratio of the edge emphasis and the smoothing can be continuously changed for a characteristic amount of the original, the erroneous judgement to the area of the vague decision is inconspicuous and the discontinuity which occurs due to the switching of the processes can be also eliminated. That is, in the case where the algorithm of the fuzzy inference was used for the separation of the image area and the processes based thereon, a conclusion is output by synthesizing the functions of a plurality of parameters. Therefore, for instance, even if one input parameter is wrong, the error of the conclusion is lightened. When reproducing an image such that the erroneous judgement is conspicuous, an extremely typical effect of the prevention of the erroneous judgement is obtained. On the other hand, in the case of executing the fuzzy inference, it is easily possible to increase the number of input parameters which are necessary to obtain a certain conclusion and to synthesize the conclusions. The invention is effective in the portion of the image process which can be easily influenced by various conditions. That is, for instance, a conclusion can be more accurately obtained by using other parameters such as intrablock average density, continuity of image, and the like in addition to the edge amount and count value in the embodiment. The invention can be also obviously applied to the processes of a color image as well as a black and white image. In this case, the apparatus can be constructed as shown in Fig. 1D. Although the operation of each section is substantially similar to that in Fig. 1A, in Fig. 1D, the processes are executed in parallel the read signals of R, G, and B. On the other hand, since the luminance data is used for edge detection and screen detection, a luminance data generation circuit 450 executes the calculation of (0.3R + 0.59G + 0.11B) for input signals R, G, and B. The signal of (R + G + B)/3 or only the G signal can be also used in place of the luminance data. On the other hand, if the parameters are properly set with respect to each of three primary colors of R, G, and B and a judgement circuit is provided for each of the color signals and a plurality of judgement results are used, the discriminating accuracy is further improved. <Embodiment 2> As a second embodiment, there is considered a case of using filters of 5 x 5 as shown in Figs. 5A and 5B as filters for the smoothing and the edge emphasis. Even in this case, in a manner similar to the above embodiment, the weighted mean of outputs of both of the filters is calculated by the equation (1) by setting the weighted mean ratio to α. As will be obvious from Fig. 5, the case of α = 19/20 is equivalent to the filter of Fig. 5C. Therefore, membership functions of the conclusion section are as shown by reference numerals 601 to 603 in Fig. 6. Particularly, the peak of the function 602 corresponding to a photograph appears when α = 19/20 instead of α = 9/10 in Fig. 3. As mentioned above, according to the embodiment, the effects of the smoothing and the edge emphasis can be enhanced by enlarging the matrix size of the filter. As described above, according to the invention, the proper image process can be executed in accordance with the feature of the input image and the picture quality of output image is remarkably improved. <Embodiment 3> According to the third embodiment of the invention, by providing a plurality of linear masking circuits and a color judgement circuit, a plurality of masking correction outputs are synthesized in accordance with the color of an image original. The color judgement circuit can smoothly synthesize them by using membership functions. The third embodiment of the invention will now be described hereinbelow with reference to the drawings. Fig. 8 is a block diagram in which the constructions of the invention are combined every process. In the diagram, reference numeral 1201 denotes an image input device comprising a CCD color image sensor, a sample and hold circuit, an A/D converter, and the like. The image input device 1201 outputs digital color separation signals Red, Green, and Blue of an image original to be copied. Reference numeral 1202 in the diagram indicates a color reproducing system comprising a density conversion circuit 1203 and a color conversion circuit 1204 to execute the conversion of R, G, B-Y, M, and C. The R, G, and B color separation signals which were output from the image input device 1201 are input to the density conversion circuit 1203 and are converted from the luminance signals to the density signals in accordance with the equation (2). Thus, the C, M, and Y signals are generated from the R, G, and B signals, respectively. Then, the density signals C, M, and Y are input to the color conversion circuit 1 (1204) in the diagram, by which a black signal K is produced from the minimum value MIN(Y, M, C) of the Y, M, and C signals. The masking process is executed to correct the output characteristics (printing characteristics such as reduction of saturation or the like upon color mixing of the color inks in the case of, for instance, the ink jet system) of an image output device 1209. The color conversion circuit 1 of the invention will now be described in detail with reference to Fig. 9A. As also already described in the conventional example, the Y, M, C, and K signals including the black signal K which was reproduced from the minimum value among the Y, M, and C signals by the black component extraction circuit 1301 are input to a masking circuit 1303 including high-order terms such as Y 2 and M 2 terms or the like. The color conversion is executed by the polynominal including the square term and product term as shown by, for instance, the equation (1) in the conventional example. As mentioned above, by including the high-order terms, masking coefficients indicative of the good color reproducibility are set with respect to the achromatic color. On the other hand, the output signals Y, M, C, and K of the black component extraction circuit 1301 are similarly input to a masking circuit 1304 and the masking operations as shown by the equations (3) are executed. Masking coefficients b ij (i ≤ ij ≤ 3) and b 44 are determined by a method whereby values such as to optimize the color reproducibility for an achromatic reading original are calculated by a method of least square and an UCR (100% UCR) process near the full black is also performed, thereby determining the masking coefficients. That is, the cofficients such as b 44 ~ 1 b 11 ~ b 22 ~ b 33 ~ 0 are set. On the other hand, the output signals Y, M, and C of the black component extraction circuit 1301 are input to a masking circuit 1302 and the masking operation shown by the equation (4) is executed. Masking coefficients d ij (1 ≤ ij ≤ 3) are set by the optimization of the color reproducibility for a reading original of the skin color. That is, values such as to optimize the reproducibility for the skin color are previously calculated as d ij . On the other hand, the skin color is reproduced by three colors of Y, M, and C without providing the term of K (black) in the output. This is because in the case of the skin color, if the black toner is mixed, muddiness of color occurs and it is unpreferable. As mentioned above, the color conversion circuit 1204 comprises the three kinds of masking circuits 1302, 1303, and 1304 whose masking coefficients have peculiar meanings, respectively. In the masking circuit 1304, the masking coefficients such as to become uniform and minimum over the whole color space range are set and signals Y 2 , M 2 , C 2 , and K 2 are output. In the masking circuit 1303, the masking coefficients which exhibit the extremely good color reproducibility with respect to the achromatic color are set and signals Y 3 , M 3 , C 3 , and K 3 are output. In the masking circuit 1302, the masking coefficients which exhibit the extremely good color reproducibility with regard to the skin color are set and signals Y 1 , M 1 , and C 1 are output. The output signals (Y 1 , M 1 , C 1 ), (Y 2 , M 2 , C 2 , K 2 ), and (Y 3 , M 3 , C 3 , K 3 ) from the color conversion circuit 1 (1204) are then input to a color conversion circuit 2 (1208). The conversion of the following equation (5) is executed by using weighting coefficients α, β, and γ, which will be explained hereinlater. Output signals of Y 4 , M 4 , C 4 , and K 4 are sent to the image output device 1209 and are printed and recorded. A method of deciding the weighting coefficients α, β, and γ will now be described. The color separation signals of R, G, and B from the image input device 1201 are input to the color reproducing system 1202 and to a weighting coefficient setting system 1205. The weighting coefficient setting system 1205 mainly comprises two circuits of a color signal conversion circuit 1206 and a weighting coefficient determination circuit 1207. The color signal conversion circuit 1206 converts the R, G, and B signals into Y, I, and Q signals of the NTSC system. Practically speaking, the linear conversion is executed by the following equation (6) in a manner such that the R, G, and B signals of the CCD outputs are equalized to signal outputs of R', G', and B' based on the NTSC system. As is well known, since the conversion from the R, G, and B signals by the NTSC system into Y, I, and Q signals is expressed by the following equation (7), the conversion from the R, G, and B signals of the CCD outputs into the Y, I, and Q signals is as shown by the following equation (8) from the equations (6) and (7). The conversion of the equation (8) is performed by the color signal conversion circuit 1206. Further, the I and Q signals are input to the weighting coefficient determination circuit 1207, by which the color tone is discriminated and the weighting coefficients α, β, and γ are output. That is, when the color of reading image is achromatic, α 1 in the equation (5) is set and the output values of the masking process which were optimized by the achromatic color are transferred to the recording device. On the other hand, if the color of reading image is the skin color, γ 1 in the equation (5) is set and the output values of the masking process which were optimized by the skin color are transferred to the recording device. A method of deciding the weighting coefficients α, β, and γ from the I and Q signals will now be described. Fig. 10 shows a distribution diagram (area A) of the I and Q signals by the achromatic image and a distribution diagram (area B) of the I and Q signals by the skin color image. It is now assumed that the levels of "blackness" are not equal in any of the I and Q coordinates in the area A in the case of black and the degree of "blackness" which is recognized by a measuring person differs. In other words, everyone recognizes "black" at an origin (I = Q = ) of the I and Q axes as "black or gray or achromatic color". However, as the position approaches the boundary line in the area A, there is a possibility such that the color is regarded to be "almost black", "slightly black", or the like. Further, on the boundary line, there is also a possibility such that the color is recognized to be "red" such as "black-like red" or the like. Therefore, in order to quantize the "black-like" color, an aberration amount Δ of the impression from true black is expressed by an axis of abscissa and numerical values of 0 to 1 are set on an axis of ordinate and membership functions as shown in Fig. 7A which are expressed by degrees (grades) are made. Practically speaking, the values of I and Q at the boundary line in the area A in Fig. 10 are set to the maximum value of the aberration amount Δ. The values of I and Q at the center of the area A are set Δ = 0 and the value of Δ is standardized by 0 to 1 and set to the axis of abscissa. Practically speaking, now assuming that the I and Q signals of a certain color C are set to values of I i and Q i , I A , Q A : Values of I and Q on the boundary line I C , Q C : Values of I and Q at the center of the area With respect to the achromatic color, since the center is set such that I C = Q C = 0, the equations (9) can be rewritten as follows. ΔI = I i I A ΔQ = Q i Q A For instance, as shown in Fig. 7A, five membership functions are set by giving the following label names: (1101) "(the aberration amount of the color is) (very small = VS)" (1102) " (small = S)" (1103) "(medium = M)" (1104) "(large = L)" (1105) "(very large = VL)" For instance, the function of "the aberration amount is) very small" is a function such that the degree decreases as the value of A increases in a manner such that when Δ = 0, the degree = 1 (that is, everyone admits the color to be black) and that when Δ = 0.5, the degree = 0. The function of "medium" is a function such that when Δ = 0.5, the degree = 1. Now, assuming that the value of Δ of a certain color C is set such that ΔI = 0.44 and ΔQ = 0.2 as shown in Fig. 7A, the values of the respective functions are set as follows (refer to Figs. 11A to 11E). VS (ΔI) = 0.125 VS (ΔQ) = 0.6 S (ΔI) = 0.625 S (ΔQ) = 0.9 M (ΔI) = 0.9 M (ΔQ) = 0.4 L (ΔI) = 0.375 L (ΔQ) = 0.0 VL (ΔI) = 0.0 VL (ΔQ) = 0.0 Those function values are now applied to the rule block (Fig. 7B) which specifies the fuzzy rule. The rule block is written in a format of "IF ~ THEN ---". It is now assumed that ~ is called a condition part and --- is called a consequence part. Now, when considering the rule 1 ○, there are two clauses such as I = VS (ΔI) and Q = VS (ΔQ) and their degrees are set such that VS (ΔI) = 0.125 and VS (ΔQ) = 0.6. Therefore, the degree of the condition part has the minimum value (= 0.125) of each clause. Further, the degree of the consequence part is also set to 0.125. The consequence parts have membership functions as shown in Fig. 7C and function names of VS to VL are given to them. The function of the consequence part of the rule 1 ○ is obtained by cutting away the membership function VL at VL = 0.125 and the conclusion for the rule 1 ○ is set to a hatched portion 1601 in Fig. 12. Similarly, the consequence parts of the rules 2 ○ to 5 ○are shown below. 1 ○ ω = 0.125 2 ○ ω = 0.625 3 ○ ω = 0.4 4 ○ ω = 0.0 5 ○ ω = 0.0 A barycenter x of the area of a black bold frame in Fig. 6 which surrounds the portion shown is then obtained. The value of x is set to a weighting coefficient γ for the achromatic color. In Fig. 12, x = γ = 0.6. Similarly, the weighting coefficient with respect to the skin color is calculated from an area B and is set to α. From the above α and γ, α, β, and γ or α', β', and γ' are obtained. Those weighting coefficients are transferred to the color conversion circuit 1208 in Fig. 8 and are used as an input signal to the image output device 1209 by the equation (5). Fig. 9B shows a construction of the weighting coefficient determination circuit 1207. Reference numerals 1320 and 1321 denote aberration amount calculation circuits (A) and (B) for outputting aberration amounts ΔI 1 and ΔQ 1 from reference black and aberration amounts ΔI 2 and ΔQ 2 from reference skin color for the input signals I and Q, respectively. Reference numerals 1322 and 1323 denote fuzzy inference units for executing the fuzzy inference with respect to "black likelihood" and "skin color likelihood", respectively. The fuzzy inference units output γ indicative of the black likelihood and α indicative of the skin color likelihood. Reference numeral 1324 denotes a subtraction circuit for executing the calculations of the equations (11) and for sending α, β, and γ to the color conversion circuit 2. Fig. 9C shows constructions of the fuzzy inference units 1322 and 1323. Reference numerals 1325 to 1328 denote RAMs. The RAM (A) 1325 and the RAM (B) 1326 output the degrees which belong to the membership functions VS, S, M, L, and VL with respect to ΔI and ΔQ, respectively. The RAM (C) 1327 outputs the degrees which conform with the rules to 1 ○ to 5 ○. The RAM (D) 1328 outputs the barycenter x on the basis of the equation (10). The barycenter x is used as a result of the fuzzy inference. The construction of the fuzzy inference unit can be modified. In a manner similar to Fig. 3H, the barycenter x can be also directly output by inputting the state amounts ΔI and ΔQ. On the other hand, in a manner similar to Fig. 3I, the software processes can be also executed by using the CPU. As an image output device 1209, it is possible to use various kinds of printers for outputting a color image such as color laser beam printer, color ink jet printer, color thermal copy transfer printer, and the like. Each of the color conversion circuits 1 and 2, color signal conversion circuit 1206, and weighting coefficient determination circuit 1207 can be constructed by a ROM or RAM. It is sufficient to make the contents of the table correspond so as to output the output signals such that the processing results as mentioned above can be obtained for the input signals. On the other hand, instead of using any ROM and RAM, it is also possible to use a circuit such as to actually execute arithmetic operations any time in a manner similar to the case described in the embodiment 1. As described above, according to the embodiment, by continuously switching the color correcting processes by the membership functions in consideration of the vagueness of the image reading signal, there is obtained an extremely high effect such that the color reproducibility of the image can be improved without causing any connecting portion (e.g., pseudo contour) upon switching of the processes. Particularly, according to the embodiment, the color tone which has conventionally been dominated by the sense of human being, for instance, the skin color likelihood or achromatic color likelihood are expressed as membership functions and the control to be executed in accordance with the "likelihood" is determined as a "rule". Therefore, the delicate color tone control which has conventionally been difficult to be automated can be easily executed. Moreover, according to the invention, since the fuzzy inference is used to determine the output color, it is possible to easily cope with an increase in number of kinds of "likelihood" colors. That is, the number of functions to be used as parameters can be easily increased. The consequence for complicated conditions can be easily obtained. On the other hand, since the weighting coefficients α, β, and γ can be continuously changed, a smooth image can be obtained without causing a sudden change in the change portion of the color tone. Even if an error exists in a part of the input parameters to be used as a condition part of the fuzzy inference, since the inference is performed by using a plurality of conditions, the probability of the erroneous operation extremely decreases. The method of deciding the membership functions, the kinds of variables, the contents of rules, the number of rules, and the like are not limited to those in the embodiments. The algorithm of the fuzzy inference is not limited to the method of calculating the barycenter but can be also modified to a method such that an incenter or a circumcenter is calculated or the like. Although the embodiment has been described with respect to the color masking circuits, other correction circuits to correct the color tone, for instance, a gamma correction circuit, a color balance circuit, and the like can be also used. On the other hand, the input color component signals are also not limited to R, G, and B. Y, I, and Q signals, Y, M, and C signals, and the like can be also used. Similarly, the output signals are also not limited to the Y, M, and C signals for printing but the NTSC signals of the video signal can be also used. On the other hand, the detecting means can also detect the color in the R - G - B space instead of the I - Q space. <Embodiment 4> In the embodiment 3, two kinds of dedicated masking circuits for reproduction of black and for reproduction of skin color have been provided. However, in Fig. 9, by further increasing the dedicated masking circuits for reproduction of red and blue, for instance, the color signal conversion circuit 1206 and weighting coefficient determination circuit 1207 properly decide I A , I C , Q A , and Q C in the equations (9) for standardization as they are, thereby further raising the reproducibility with respect to a target color and enabling the red likelihood color, blue likelihood color, and the like to be preferably reproduced. <Embodiment 5> In the embodiment 3, the R, G, and B signals have been converted into the Y, I, and Q signals of the NTSC system and input to the weighting coefficient determination circuit. However, in place of the Y, I, and Q signals, signal ratios G/R and B/R of the R, G, and B signals can be also used. At this time, for example, the center of black is set to G/R = B/R = 1. The equations (9') are expressed as follows. ΔG/R = (G/R) i - 1 (G/R) A - 1 , ΔB/R = (B/R) i - 1 (B/R) A - 1 As described above, according to the invention, the color image processes of an extremely good color reproducibility can be executed. The present invention is not limited to the foregoing embodiments but many modifications and variations are possible within the scope of the appended claims of the invention.

Claims: An image processing apparatus comprising: input means (405,406,407) for inputting an image signal representing an image; spatial filtering means (103,105) for processing the input image signal; and detecting means (106,107;450) for extracting a parameter relating to a characteristic of the input image signal to distinguish photgraphic screen and character originals based on the image contrast, characterised in that the detecting means include means for extracting from said image signal a first parameter (e) which denotes the edge amount and a second parameter (c) which is different from said first parameter and concerns the characteristics of the image and in that the apparatus additionally comprises control means (108,109,110,111,112) including: memory means (310-314) for storing a plurality of membership functions relating to each of said first (e) and second (c) parameters; rule storage means (310-317) for storing a plurality of rules representing relationships between said first (e) and second (c) parameters; and wherein control means are adapted to infer a control parameter (α) by using a fuzzy inference method in accordance with said stored membership functions and said plurality of rules and to control the output of said spatial filtering means (103,105) in accordance with said control parameter (α). An apparatus according to claim 1, wherein the spatial filtering means comprise first and second means (103,105) for carrying out separate processing on the input signal, and the control means are adapted to use the control parameter to weight the respective outputs of said first and second means. An apparatus according to claim 2, wherein said first spatial filtering means is a smoothing circuit (103) and said second spatial filtering means is an edge emphasise circuit (105). An apparatus according to claim 3, wherein the first and second spatial filtering means comprise first (103) and second (105) filters connected in parallel and the control means includes combination means (109,110,112) for adding a weighted output of each filter (103,105), the weighting being in accordance with the control parameter (α). An apparatus according to claim 4, wherein said combination means comprises: multipliers (109,110) connected to the output of each filter, the multiplication being determined in relation to the controlling parameter (α); and an adder (112) for adding the outputs of the multipliers. An apparatus according to any preceding claim, wherein said control parameter (α) is calculated by use of a ROM look-up table (310,312;313). An apparatus according to any of claims 1 to 5, wherein said control parameter (a) is calculated by performing calculations in a memory (314) on the first and second parameters under the control of a CPU (315). A photocopier comprising an image processing apparatus according to any preceding claim.

Inventors: KAWAI TAKASHI, OUTA KENICHI, KAWAI, TAKASHI, OUTA, KENICHI, KAWAI, TAKASHI, C/O CANON KABUSHIKI KAISHA, OUTA, KENICHI, C/O CANON KABUSHIKI KAISHA
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1990
Main classification: G06T5
IPC codes: H04N1