JPH0556068B2 - - Google Patents

Description

[Detailed description of the invention]

<Technical Field> The present invention relates to an image processing device such as a digital copying device or a facsimile device that handles images as electrical signals. <Prior art> With the development of digital equipment, so-called digital copying devices, which generally sample images using CCD sensors and output the digitized data from digital printers such as laser beam printers to reproduce the images, have It is becoming widely used as an alternative to analog copying machines. In order to reproduce halftone images, such digital copying apparatuses generally perform tone reproduction using a dither method or a density pattern method. However, this method has the following two major problems. (1) If the original is a halftone image, the copied image will have periodic striped patterns that are not present in the original. (2) If the manuscript contains line drawings, characters, etc.
Dither processing causes edges to become cut off, reducing image quality. The phenomenon (1) is called a moiré phenomenon, and its causes are thought to be: (A) Moiré caused by the halftone original and input sampling; (B) Moiré caused by the halftone original and the dither threshold matrix. The phenomenon in (A) is caused by a halftone dot frequency _O (=1/P _p ) [PEL/mm] determined by the halftone dot pitch P _O [mm] of the halftone original, which is n times higher frequency n _O [PEL/mm]. From the input sampling frequency _S (1/P _S ) [PEL/mm] found from the input sensor pitch P _S [mm], the beat frequency becomes △ = | _S −n _O | [PEL/mm] ...(1) This results in moiré. (B)
This phenomenon occurs when the dither threshold is arranged in a concentrated dot type such as fatting type, and the output image also has a pseudo halftone structure, which causes a beat between the input halftone dot and the original. Exhibits a moiré phenomenon. P _D the repetition period pitch of the dither threshold value on the recording paper.
[mm], the spatial frequency is _D = 1/P _D [PEL/
mm], and the most prominent beat frequency is △=| _O − _D |[PEL/mm]...(2). Of the two moiré phenomena mentioned above, the one that occurs most strongly is (B)
It is the one who is. In the phenomenon of (A), this is generally n = 3 to 6 as n of the n-times harmonic of the halftone original, and the transfer function (MTF) of the optical system etc. leading to the sensor is
The contrast of the moiré fringes is also low because it drops considerably at that frequency. Moiré phenomena caused by such causes significantly degrade the quality of output images. For this reason, various countermeasures and studies have been carried out for a long time. For example, the random dither method can remove moiré, but graininess appears and the image quality deteriorates. J.
Opt.Soc.Am., Vol.66, No.10, October 1976
Proposed by Paul G. Roetling, shown on P985
ARIES compares the average value of the density before and after binarization and applies a feedback to the threshold value to make them equal, but this method requires complicated hardening and is not effective enough in moiré removal. Not. On the other hand, the re-halftone method described by Takashima et al., "Half-dot conversion of mixed text/photo images", IEICE Yoshimai 83-3 P13, blurs the half-tone image (or averages the surrounding pixels). The moiré caused by halftone redotting with the pattern is removed, and there is little grainy noise. However, the resolution cannot be avoided due to blurring (or averaging with surrounding pixels). That is, if you try to remove moire, the resolution will decrease, and if you try to maintain the resolution, moire will not be removed. Therefore, it is essential to extract only the halftone image area in advance and apply the method only to that part. For this reason, a so-called image area separation technique is required. At the current level of image area separation technology, it is difficult to obtain a high-accuracy and high-speed method, especially a method suitable for implementation in hardware, and it is difficult to realize the above-mentioned method. Even if an image area separation technique were to be obtained, such a technique would not be fully satisfactory because even high frequency components within the image would be averaged and smoothed. On the other hand, regarding problem (2), the characters and line drawings on the document are fragmented by dithering, and the print quality is degraded because the edges in particular become cut off. This phenomenon is caused by the dither pattern described above.
This is particularly noticeable in dot-concentrated types such as Fatting type. <Objective> An object of the present invention is to provide an image processing apparatus that can eliminate the two drawbacks described above and reproduce images with high quality and high definition. That is, in the present invention, moiré development that occurs in halftone originals is removed, characters and line drawings are prevented from being cut into pieces, and high-frequency components in image areas are prevented from deteriorating. It happened. Furthermore, this method can be realized with a simple circuit configuration and can be provided at low cost. <Example> (Basic configuration) FIGS. 1 to 6 The basic configuration of an image processing apparatus of this example is shown in FIG. 1. This image processing device is composed of an edge detector a, an edge enhancer b, a smoother c, and a mixer d. The edge detector a detects the edges of characters, line drawings, and images as described later, and the halftone dots of the halftone image are given a spatial frequency characteristic that does not detect them as edges. The edge enhancer b outputs the original image or an edge-enhanced image signal obtained by mixing the original image and edges at a certain ratio. The smoother c smoothes the image. The mixer d outputs the edge-enhanced image and the smoothed image at different mixing ratios depending on the signal from the edge detector. In this way, the halftone dots of the halftone dot image are determined to be non-edge areas, and smoothing is performed to average them and prevent moiré. Furthermore, the edges of characters, line drawings, and images are determined to be edge areas, and the edges are emphasized to prevent halftone dotting of characters and reduction in sharpness of images. Furthermore, since the edge area and the non-edge area are continuously connected, there is no texture change at the boundary. Next, the principle of this embodiment will be explained from the perspective of frequency characteristics. First, the number of screen lines for the halftone image of a document is usually 120 to 150 lines for black and white, and 133 to 175 lines for color. Moiré is most likely to occur when the screen angle is between 0 degrees and 45 degrees. Further, the dot pitch in the main scanning direction during line reading is maximum at 45 degrees and has a low spatial frequency, and minimum when it is 0 degrees and has a high spatial frequency. Screen angle is 0 degrees
Table 1 shows the spatial frequencies at 45 degrees.

[Table] The frequency characteristics of such a halftone image have peaks at the fundamental frequency and its high frequencies, as shown in Figure 2a.
Further, the frequency characteristics of a character image and a continuous tone photographic image are as shown in FIGS. 2b and 2c, respectively. For such a mixed image of characters, photographs, and halftone dots, the spatial filters of the edge detector, edge enhancer, and smoother of this embodiment have frequency characteristics that satisfy the following conditions. Condition 1: The peak frequency of the spatial filter of the edge detector is set to be lower than the first harmonic frequency of the halftone image. Condition 2: The peak frequency of the spatial filter of the edge enhancer is set to be higher than the peak frequency of the spatial filter of the edge detector. Condition 3: The frequency characteristics of the spatial filter of the smoother are sufficiently reduced at the first harmonic frequency of the halftone image. Also, the frequency is sufficiently lowered to correspond to the period of the output dither. There are various types of spatial filters for edge detection, but if the matrix size, which affects the scale of the hardware circuit, is held constant, the first-order differential filter has a peak at a lower frequency than the second-order differential filter. . However, the second-order differential filter does not have directionality, but the first-order differential filter has directionality, and the square root of the sum of the squares of the slopes in at least two directions, or as an approximate expression thereof, the absolute value of the slopes in at least two directions. It is necessary to take the sum of the values or the maximum value of the absolute values of the slopes in at least two directions. Furthermore, the first-order differential is more resistant to point noise than the second-order differential. As described above, it is better to use a first-order differential filter as the spatial filter for the edge detector a. In addition, as a spatial filter for edge enhancer b, there is no directionality and the peak is at a higher frequency.
A second-order differential filter is better than a first-order differential filter. For simplicity, the relationship between the frequency characteristics of the various spatial filters as described above was calculated using a one-dimensional fast Fourier transform (FFT).The results are shown below. For example, the reading sampling interval of the input system is 1/16mm, and the output system is
Calculation is performed using a 4×4 dither matrix at 16 dots/mm. The period of the dither pattern is 41/mm in terms of spatial frequency. Furthermore, in reading with 1/16mm sampling, only frequencies up to 81/mm can be detected due to the sampling theorem. If the matrix size is 5 x 5, the 1st dimension of the quadratic differential filter (-1, 0, 2, 0, -1)
The FFT is shown in Figure 3, and the first-order differential filter (-1,
0, 0, 0, 1) is shown in Figure 4, and another first-order differential filter (-1, -1, 0, 1, 1)
The one-dimensional FFT of is shown in Figure 5. The peak positions are 4 1/mm and 2 1/mm, respectively.
mm, 2.5 1/mm. Comparing this with the spatial frequency of the halftone image in Table 1, the first-order differential filter satisfies condition 1 for all lines in Table 1, but the second-order differential filter satisfies condition 1 for 120 and 133 lines.
Condition 1 cannot be satisfied at 45°, and halftone dots are detected as edges. Comparing two types of first-order differential filters, we found that the pulse width was increased (-1, -1, 0,
1, 1) is better. This is because the larger the pulse width, the smaller the intensity of the second peak, and the larger the pulse width, the wider the edge region (edge emphasis applied to this region) can be detected. Edge detection is set to the first-order differential filter (-1, -1, 0, 1, 1), and edge emphasis is set to the second-order differential filter (-1, 0, 2, 0, -
1), each peak frequency is 2.5
Condition 2 is satisfied with 1/mm and 4 1/mm. In other words, a wide area for edge enhancement is extracted by edge detection, and a spatial filter that sharpens the edges is used for edge enhancement. Next, apply a 5×5 smoothing filter (1,
1, 1, 1, 1) is shown in Figure 6.
Fundamental frequency of halftone image of 120 lines 45° or more, 3.341
The strength decreases above 1/mm. Also 4×
Pitch of dither matrix of 4, 4 1/mm
The strength is sufficiently small and satisfies condition 3. In this example, by using spatial filters with frequency characteristics such as the above-mentioned conditions 1 to 3 for the edge detector, edge enhancer, and smoother, flat parts of the image and halftone dot images are determined to be non-edge areas and smoothed. The edges of characters, line drawings, and images are determined to be edge areas, and the edges are emphasized. Further, the boundary between the edge region and the non-edge region is continuously connected by changing the mixing ratio in the mixer according to the signal from the edge detector. As described above, moiré in halftone images is prevented, halftone dot formation of characters and reduction in image sharpness are prevented, and discontinuous changes in texture between edge areas and non-edge areas do not occur. Furthermore, since the matrix size of the spatial filter does not need to be large, the scale of the hardware circuit can be reduced, which is advantageous for LSI implementation. FIG. 7 is a block diagram showing an embodiment of the present invention.
S1 is the input image signal, 1 is 1 of the input image signal S1
The first differential value detector detects the absolute value of the second differential value.
Corresponds to figure a. S2 is a differential signal connected to the output of differential value detection section 1, 2 is a control signal generator that generates control signals S3 and S4 from differential signal S2, S3 is the output of control signal generator 2 and is a control signal, and S4 is also a control signal. The output of the signal generator 2 is a complementary control signal to the control signal S3, and 3 is a smoothing processing section for smoothing the input image signal S1, which corresponds to FIG. 1C. S6 is a smoothed image signal smoothed by the smoothing processing unit 3, 4 is a multiplier that takes the arithmetic product of the smoothed image signal S6 and the control signal S3, S7 is the output of the multiplier 4, and 5 is the input An edge enhancement unit that emphasizes the edge portion of the image signal S1; S8 is an edge signal of the edge enhancement unit 5; S9 is a constant given from the outside; 6 is a multiplier that takes the arithmetic product of the edge signal S8 and the constant S9;
S10 is an edge signal output from the multiplier 6;
Reference numeral 7 denotes an adder which calculates the arithmetic sum of the edge signal S10 and the input image signal S1, and the edge emphasizing section 5, multiplier 6, and adder 7 constitute the edge emphasizing device b in FIG. S11 is an edge-enhanced image signal which is the output of the adder 7, 8 is a multiplier for calculating the arithmetic product of the edge-enhanced image signal S11 and the control signal S4, and S12
is the output of multiplier 8, 9 is the output S7 and output S12
An adder S13 which takes the arithmetic sum of is the output of the adder 9 and is a processed image signal. Here, multipliers 4, 8 and adder 9 constitute mixer d in FIG. Here, in the edge emphasizing section 302 corresponding to the edge emphasizing device b, the edge (edge detect)
A multiplier 6 multiplies the output of the emphasizing section 5 and the aforementioned control signal S9. It goes without saying that the multiplier 6 can be constructed from a ROM or the like, and the control signal S9 may be a coded signal instead of the multiplication coefficient itself. When the pixel of interest in image processing is A, an adder 7 adds a value multiplied by a certain coefficient of the pixel of interest and the edge detect, thereby edge-emphasizing the pixel of interest. The mixing section 305 is a circuit section that mixes the output of the edge enhancement section 302 and the output of the smoothing processing section 3 at an appropriate ratio.
According to the output of the differential value detection unit 1 input to
3, S4 is output. S3, S4 as described later
are complementary control signals, but are not necessarily limited to them. The characteristics of S3 and S4 can be freely selected and set using the control signal S5. In the multiplier 8, the output of the edge emphasis section is multiplied as determined by S4; in the multiplier 4, the output of the smoothing section is multiplied as determined by S3;
The outputs of multipliers 4 and 8 are added together by adder 9, and this becomes the image processing output. The block diagram of FIG. 7 can also be expressed by the following equation. First, the differential value detection section 1 and the control signal generator 2 perform calculations as shown in Equation 1 below. E=f(| 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 -1-1-1-1-1 -1-1-1-1-1 ^*1・I |+|1 1 0 -1-1 1 1 0-1-1 1 1 0-1-1 1 1 0-1-1 1 1 0-1-1 ^*2・I |) ... (Equation 1) Here I is the input image data, E is control signal S4
It is. f is a function that normalizes the control signal S4 to a maximum value of 1 and performs gamma conversion. The following output is obtained from the adder 7. G=±+k ₁ 0 0-1 0 0 0 0 0 0 0 -1 4 0-1 0 0 0 0 0 0 0-1 0 0・I
...(Formula 2) G is the output of the adder 7, and _k1 is a constant of the constant signal S9. Then, the following output is obtained from the smoothing processing section 3. H=11111 11111 11111 11111 11111·I (Formula 3) H is the output of the smoothing processing section 3. Therefore, the value Out of the output S13 of the adder 9 can be expressed by the following equation 2. 0= ^E _. 11111 11111 11111 11111 11111 ^*4 I ) ...(Formula 4) The part in brackets [ ] shown in the above formula is a kernel that takes convolution with the image signal I. Kernel*1～
*4 can be modified in various ways, examples of which are shown in Table 2.

【table】

[Table] FIG. 8 shows an example of the characteristics of the function f of the control signal generator 2. In E=f(x), E=0 for 0x<0.2 E=1.67x−0.33 for 0.2x<0.8 E=1 for 0.8x1. However, both input and output signals will be standardized to 0 to 1 for explanation. FIGS. 9 and 10 are diagrams for explaining the operation of the edge detecting section 1 (hereinafter referred to as differential value detecting section) based on first-order differentiation, and are shown in one-dimensional main scanning. As already explained, the differential value detection section 1 is a kind of band-pass filter, so the ninth
The input image signal S1, such as a halftone dot image with high frequency components as shown in the figure, has a kernel (-1, -1, 0,
1, 1), when convolution is performed in the main scanning direction, the output signal S2 becomes a small value of 0.1 to 0.2. On the other hand, in the case of a relatively low-frequency input image signal (for example, vertical lines of characters), as shown in FIG.
Due to similar convolution, the output signal S2 takes on a large value. Here, the control signal generating section 2 that performs gamma conversion sets the control signal S3 to 1 and the control signal S4 to 0 when the differential signal S2 is smaller than 0.2, as shown in FIG. If the differential signal S2 is larger than 0.8, the control signal S3 is set to 0 and the control signal S4 is set to 1. Furthermore, when the differential signal is between 0.2 and 0.8, the control signal S3 and the control signal S
It changes according to the differential signal S2 so that the sum of 4 becomes 1. On the other hand, the input image signal S1 is connected to the input of the differential value detection section 1, and is also connected to the inputs of the smoothing processing section 3 and the edge detection section 5 at the same time. FIG. 11 shows the operation of the smoothing processing section 3, and for the sake of explanation, shows a one-dimensional example in the main scanning direction. In this case the kernel is (1, 1, 1, 1,
In 1), the contents are all 1 and the low-pass filter is configured to output the average of 5 pixels, and the input image signal S1 is the smoothed image signal S6.
become that way. Further, FIG. 12 shows the operation of the edge emphasizing section 5, and also shows one dimension in the main scanning direction for the sake of explanation. The kernel is (-1, 0, 2, 0, -
1), which has the well-known second-order differential edge detection characteristic, and the output S8 has 0 at the flat part and positive and negative peaks at the edge part. Furthermore, the edge signal S8 is multiplied by a constant S9 by a multiplier 6, and the edge signal S8 is multiplied by a constant S9 by a multiplier 6.
is added to form the edge emphasis signal S11. still,
Although not shown, since the edge signal S10 is slightly delayed with respect to the input image signal S1, a delay circuit is actually provided to adjust the timing between the edge signal S10, which is input to the adder 7, and the input image signal S1. By the way, in the case where the output of the differential value detection section 1 is large, that is, at the edge portion, the control signal S3 is small and the control signal S4 is large. Conversely, when the differential signal S2 is small, S3 is large and S4 is small. Also, as mentioned in Figure 8, S3
and S4 are always gamma-converted so that their sum becomes 1. Therefore, the sum of the outputs of the multipliers 4 and 8 is controlled so that when the differential signal S2 is large, the edge emphasis signal S11 has a large component, and when S2 is small, the smoothed signal S6 has a large component. Figure 13 shows this situation, where the differential signal S
2 indicates that edges are detected in a portion of the input image signal S1 excluding vibrations with a short period (corresponding to the halftone dot period). The control signal S4 is obtained by gamma-converting the differential signal S2, and all but the four peaks of the signal S2 shown in FIG. 13 are set to zero. S3 is naturally 1-S4. Furthermore, FIG. 13 also shows a smoothed signal S6 and an edge emphasis signal S11. In FIG. 13, the processed signal S13 is a signal obtained by adding S6 and S11 at the ratio of S3 and S4, smoothing the halftone dot part, and emphasizing only the edge part. Next, each block in FIG. 7 will be explained in detail. (Differential value detection unit 1) FIG. 14 is a detailed circuit diagram of the differential value detection unit 1. The output of the 5-line buffer 301 shown in FIG. 20 is input to the differential value detector 1. In the figure, 306 is a first-order differentiator, and its output 306-a is divided into a data section 306-c and a sign section 306-b indicating positive/negative. The absolute value of the data 308-a is determined by selecting either the input data and +/- inverted data by the inverter 307 or the data 306-c.
is obtained. Similarly, the absolute value of the output of the first-order differentiator 312 is output from the selector 311, 308-a and 311-a are added by the adder 309, and the sum of the first-order differential values in two directions is output from the adder 309. Ru. Next, a block diagram showing details of the first-order differentiators 306 and 312 shown in FIG. 14 is shown in FIG. 15. First, in order to explain the basic operation of this first-order differential circuit, block X in FIG. 15 will be explained. First, all shift registers in FIG. 15 are shifted in synchronization with an image transfer clock not shown in the figure. To simplify the explanation, the multipliers 243~
Assume that all 247 multiplication coefficients are 1. According to the timing chart FIG. 16, the output of the shift register 230 at t-3 is Sn, m _-1 +Sn, m _-2 , and the output of the shift register 231 at t-2 is Sn, m + Sn, m _-1 +Sn, m _{- 2} and t-1
The output of the shift register 232 at is Sn, m ₊₁
+Sn, m+Sn, m _-1 +Sn, m _-2 , and the output of adder 260 at t0 is Sn, m ₊₂ +Sn, m ₊₁ +Sn,
m+Sn, m _-1 +Sn, m _-2 . In this way, the sum of five pixels in the main scanning direction is calculated in block X. Here, by setting the multiplication coefficients of the multipliers 243 to 247 to a, b, c, d, and e, the output of the adder 260 is e·Sn,m ₊₂ +d·Sn,m ₊₁
+c・Sn, m+b・Sn, m _-1 +a・Sn, m _-2 . It can also be seen that the circuits after the shift register 223 and the circuits after the shift register 233 operate in the same manner. By the way, the first derivatives to be found are *1 and *2 in formula 1.
In a case like this, if the pixel of interest is on the nth line,
The kernel elements of the n-2 line and the n-1 line are equal, and the kernel elements of the n+1 line and the n+2 line are also equal. Therefore, the circuit size can be reduced to 1/2 by adding the image data at the n-2nd line and the n-1st line using the adder 231, and then performing first-order differential processing as shown in equations 1*1 and *2. It can be reduced to Also, n+
The same applies to the first line and the (n+2)th line. In this way, the adder 400 can obtain addition values corresponding to the kernels for five lines. In addition, when the multipliers shown in 238 to 252 have a simple multiplication value such as 1or-1or0, the first
As shown in Figure 7, it can be easily configured with an inverter 291 and a selector 292, and SL in the figure
It can be switched 1 or -1 and set to 0 using CL. In addition, to obtain the first derivative of the kernel *1 in Equation 1, the multiplication coefficients of the multipliers 238 to 242 are set to 1,
The multiplication coefficients of multipliers 243 to 247 are set to 0, and the multiplication coefficients of multipliers 248 to 252 are set to -1. To find the first derivative of the kernel *2 in Equation 1,
Multipliers 242, 238, 239, 240, 241
The multiplication coefficients of the multipliers 243 to 247 are 1, 1, 0, -1, -1, and the multiplication coefficients of the multipliers 243 to 247 are 1, 1, 0, -1, -1.
1, and the multiplication coefficients of the multipliers 248 to 252 are 1,
1, 0, -1, -1. In addition, the circuit configuration for calculating the first-order differentials such as *1 and *2 in Table 2 can be realized using FIG.
Since the operating principle is the same as in FIG. 5, the explanation of how to give coefficients to the multiplier will be omitted. (Edge Emphasizing Unit 5) Next, the edge emphasizing unit 5 is shown in FIG. As shown in FIG. 20, the input image signal S1 shown in FIG. 7 consists of data for 5 consecutive lines of image data, and the input image data input to the 5-line buffer 301 is stored in the 5-line buffer. Thereafter, five lines are simultaneously output, and the image data is output pixel by pixel in the main scanning direction in synchronization with an image transfer clock (not shown). Furthermore, as shown in Figure 21, the region of interest S in the image area is further enlarged and the pixel data of interest is
Consider the surrounding image data when Sn and m are taken. The edge enhancement unit 5 in FIG. 19 receives three lines of image data of the n-2nd line, n, and n+2nd line of the input image data S1, and sets the pixels of interest for image processing to Sn and m. . In the figure, 201 to 211 are 1-bit shift registers. Image data S1 is stored in shift register 2
01-203, 204-208, 209-211
is shifted in synchronization with an image transfer clock (not shown). This timing chart is shown in FIG. 22. In FIG. 22, the output of the shift register at a certain timing T is shown enclosed in parentheses in FIG. Adder 213 is shift register 203, 20
4,208,211 output data Sn _-2 , m, Sn,
m ₋₂ , Sn, m ₊₂ , Sn ₊₂ , and m are added, and the added data is multiplied by −1 by the multiplier 214 . pixel of interest
Sn, m are output from the shift register 206,
The multiplier 212 multiplies the signal by 4, the adder 215 adds the signal, and the adder 215 outputs the edge detect signal G·S11 as shown in Equation 2 and FIG. In addition, since the kernel elements of the n-2nd line and the n+2nd line are the same, the shift registers 209 to 21
1 may be omitted and the outputs of the (n-2)th line and (n+2)th line may be added and then input to the shift register 201. It is also clear that the edge detection section 5 can be configured by using the circuit shown in FIG. 18 and inputting the value of kernel *4 into the multiplier. (Smoothing Processing Unit 3) Next, details of the block of the smoothing processing unit 3 shown in FIG. 7 are shown in FIG. The image signal S1 consists of five lines of data that are continuous in the sub-scanning direction of the image, and the adder 271
Addition of 5 pixels in the sub-scanning direction is performed. This data is transferred to a shift register 272 to be delayed by 1 bit.
is input. The output data of shift register 272 is input to adders 277-280. The adder 272 outputs the output of the shift register 272 and its 1
The data before the pixel is added. The result of this addition is 2
278 after being latched into 74 shift registers
The adder adds the next pixel. Similarly, at time T2, the adder 280 outputs S _N , m ₊₂ +S _N , m ₊₁ +S _N , m+S _N , m _-1 as shown in FIG.
+S _N , m _-2 is output (S _N , j=Sn _-2 , j+
Sn _-1 , j+Sn, j+Sn ₊₁ , j+Sn ₊₂ , j). In this way, when the pixel of interest is Sn, m, the pixel sum shown by *3 in equation 2 is output from the adder 280 and divided by the total number of pixels by the divider 281 to obtain smoothed data. FIG. 26 shows a circuit that performs weighted smoothing as shown in FIG. 25. Although the operation timing etc. are the same as in FIG. 23, in FIG.
Smoothing as shown in FIG. 25 is performed by weighting by 360. This smoothing processing section adds all the added values in the image sub-scanning direction and then adds them in the image main scanning direction, so the circuit scale can be reduced. (Other Examples) In this example, the kernels of the differential value detection unit, smoothing processing unit, and edge enhancement unit were set to 5×5.
Depending on the number of lines targeted for moiré removal, 3×3 may be sufficient, or 5×5 or more may be required. Furthermore, in this embodiment, the smoothed signal S6, which is the output of the smoothing processing section 3, and the edge emphasis signal S11, which is the output of the adder 7, are added in proportion according to the output of the gamma conversion section 2. The input image signal S1 may be used instead of the signal S11. In this case, although it is somewhat inferior to the present embodiment with respect to characters and line drawings, it has the advantage that the apparatus can be significantly simplified and the same effect as the present embodiment can be obtained in terms of suppressing moiré. In addition, an edge emphasizing unit 302 shown in FIG. 7, which is constituted by an edge emphasizing unit 5, a multiplier 6, and an adder 7, can vary the center of the kernel *4 of the edge emphasizing unit 5 by a constant S9. In such a configuration, the multiplier 6 and the adder 7 are unnecessary. Further, in this embodiment, the constant S9 is variable from the outside, but it may be fixed internally. Furthermore, in the embodiment of the present invention, the characteristics of the control signal generator 2 that performs gamma conversion have been described as shown in FIG. 8, but FIGS. 27 a to 27 c show modified examples of the characteristics of the gamma converter 2. . In FIG. 27, only the characteristics of the control signal S4 are shown, but the control signal S3 is expressed as S3=1-S4. FIG. 27-a has the following characteristics: S4=0, but 0<S2<0.5, S4=1.0, but 0.5<S2<1.0, and is particularly characterized in that the circuit of the gamma conversion section can be constructed easily. FIG. 27-b has the characteristic S4=-arctan(k.S2+k), and is particularly characterized in that the connection between the smoothed signal and the edge emphasis signal is smooth. Figure 27-c has the following characteristics: S4=0, but 0<S2<0.25 S4=0.33, but 0.25<S2<0.5 S4=0.67, but 0.5<S2<0.75 S4=1.0, but 0.75<S2<1.0; Compared to the embodiment shown in the figure, the circuit is relatively simple, and the connection between the smoothed signal and the edge emphasis signal is smoother than when the characteristics of the gamma conversion section shown in FIG. 27-a are used. There is a characteristic that More specifically, for example, the well-known Prewitt edge detection method or Sobel edge detection method may be used as a differential value detection section. Furthermore, a Laplacian may be used as the edge detection section. Furthermore, in the Prewitt edge detection method, the Sobel edge detection method, or the Laplacian, spatial filter processing is usually performed using a 3x3 kernel, but the essence of the present invention does not apply even if the kernel size is expanded to a size other than 3x3. It has no impact. <Effects> As described above, according to the present invention, it is possible to prevent the occurrence of moiré when reproducing a halftone image, and at the same time, characters and thin lines can be faithfully reproduced by edge enhancement. Furthermore, the edge detection means, smoothing means, and edge emphasis means operate by the convolution method, and the size of the kernel required for each convolution is made equal, so that the required number of line buffers can be made equal and the same line buffers can be used. It is possible to design a smaller circuit scale.

[Brief explanation of drawings]

FIG. 1 is a diagram showing the basic concept of an embodiment of the present invention;
Figure 2 is a frequency characteristic diagram of various images, Figure 3,
4 and 5 are frequency characteristic diagrams of various differential filters, FIG. 6 is a frequency characteristic diagram of a smoothing filter, FIG. 7 is an image processing block diagram of this embodiment, and FIG. 8 is a control signal generation unit 2. Gamma conversion characteristic diagram, Fig. 9,
FIG. 10 is a diagram showing an example of the operation of the differential value detection section 1 that performs first differentiation, FIG. 11 is a diagram showing an example of the operation of the smoothing processing section 3, and FIG. 12 is a diagram showing an example of the operation of the edge detection section 5. , FIG. 13 is a signal waveform diagram of each part in FIG. 7, FIG. 14 is a detailed circuit diagram of the edge detection section 1, and FIG. 15 is a detailed block diagram of the first-order differentiators 306 and 312.
16 is a timing chart showing the operation of the first-order differentiator, FIG. 17 is an absolute value circuit diagram, FIG. 18 is a detailed block diagram of another example of the first-order differentiator, and FIG. 19 is a detailed block diagram of the edge emphasis section 5. 20 is a buffer circuit diagram, FIG. 21 is a diagram showing the image area, FIG. 22 is a diagram showing the operation of the edge emphasis section, FIG. 23 is a detailed block diagram of the smoothing processing section 3, and FIG.
Figure 4 is a diagram showing the operation of the smoothing processing section, Figure 25 is a diagram showing kernels for other smoothing processes, and Figure 26 is a diagram showing the operation of the smoothing processing section.
This figure is a block diagram for performing the smoothing of FIG. 25, and FIGS. 27a, b, and c are diagrams showing other gamma conversion characteristics of the control signal generating section 2. In the figure, a is an edge detector, b is an edge enhancer, c is a smoother, d is a mixer, 1 is a differential value detection section, 2 is a control signal generation section, 3 is a smoothing processing section,
4, 6, 8 are multipliers, 5 is edge emphasis section, 7,
9 indicates adders, respectively.

Claims (1)

[Claims] 1 edge detection means for detecting edge portions of the image signal, smoothing means for smoothing the image signal, edge emphasis means for emphasizing the edges of the image signal, the smoothing means or It has a means for mixing or selecting the outputs of the emphasizing means, and the detecting means, smoothing means, and emphasizing means operate by a convolution method, and the size of the kernel required for each convolution is made equal. Image processing device.