JPS61157165A - Picture processing device - Google Patents

Abstract

PURPOSE:To reproduce a picture with the high dignity and in the high detail by controlling the mixing ratio of the output of a smoothing means and the output of an edge emphasizing means by the output of an edge detecting means concerning the picture processing device of a copying machine, a facsimile equipment, etc. CONSTITUTION:A mixing device (d) is composed of multipliers 4 and 8, and an adder 9. Here, an edge emphasizing part 302 for an edge emphasizing device (b) executes the output of an edge quantity (edge detection) detecting part 5 and executes the multiplying of a control signal S9 by a multiplier 6. When the attention picture element of the picture processing is assumed as A, by an adder 7, a certain coefficient-fold value of the attention picture element and an edge detection is added, and the attention picture element is emphasized by the edge. In a mixing part 305, at the circuit to mix the output of the edge emphasizing part 302 and the output of a smoothing processing 3 by a suitable rate, S3 and S4 are outputted in accordance with the output of a differential value detecting part 1 inputted at a front step control signal generating part 2.

Description

[Detailed Description of the Invention] Technical Field> The present invention relates to a digital copying device that handles images as electrical signals;
The present invention relates to an image processing device such as a facsimile machine.

<Prior art> Generally, images are sampled using a COD sensor, etc.
Due to the development of digital equipment, so-called digital copying devices, which reproduce images by outputting digitized data from a digital printer such as a laser beam printer, are becoming widespread in place of conventional analog copying devices.

Since such digital copying devices reproduce halftone images,
Gradation reproduction is usually performed using a dither method or a density pattern method. However, in such a method, the following two
There were 6 major problems: (1) If the original is a halftone image, a periodic striped pattern that is not present in the original appears in the copied image.

(2) If the original contains line drawings, characters, etc., the edges will be cut off by the dithering process and the image quality will deteriorate.

The phenomenon (1) is called a moiré phenomenon, and its causes are considered 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 of A) is caused by the halftone dot frequency fO(-)(PE
L/mm) n times high frequency clove nfO (PEL/mm) and input sensor pitch Ps
Input sampling frequency f s (
= p2) (PEL/mm) and Δf= Ifs-n
A beat frequency fo I (PEL/mm) is generated, which becomes moiré.

The phenomenon (B) generally occurs when the dither threshold is fattin
When the dots are arranged in a concentrated dot type such as G type, the output image also has a pseudo halftone dot structure, which causes a beat between the input halftone dot document and the moiré phenomenon. If the repetition period pitch of the dither threshold value is FD (mm) on the recording paper, then the spatial frequency is io = l/Po (PEL/mm), and the beat frequency is most prominently Δf = Ifo fo I (PEL/mm). appear.

Of the two moiré phenomena mentioned above, the one that occurs most strongly is (B). This is because in the phenomenon (A), n is generally n = 3 to 6 as n of the n times higher frequency of the halftone original, and the transmission amount 1 (MTF) etc. of the optical system leading to the sensor decreases considerably at that frequency. Therefore, the contrast of moire fringes is also low.

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, a method using the random φ dither method can remove moiré, but graininess appears, resulting in a deterioration in image quality.

J, Opt, Soc, Am,, Vol, 66° Nol0
,0ctober 1976 ARI proposed by Paul G. Roetling, shown in P985
In ES, the average value of density before and after binarization is compared and feedback is applied to the threshold value to make them equal. However, this method requires complicated hardening and has a disadvantageous effect on moiré removal. not enough.

On the other hand, the re-halftone method found in IEICE Proceedings 83-3 PL3'"Hatting of Text-Photo Mixed Images" ° Takashima et al. Since halftone dots are re-dotted using a dither pattern, moiré is removed and there is little grainy noise.

However, resolution cannot be avoided due to grading (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.

This image area separation technology is highly accurate and fast at the current level - a method particularly suited to hardware implementation.
However, it is difficult to realize the above 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 the problem (2), the characters and line drawings on the document are fragmented by dithering, and the edge portions in particular are cut off, resulting in a decrease in print quality. This phenomenon is particularly noticeable when the dither pattern is of a concentrated dot type, such as the above-mentioned Fatting type.

Purpose> An object of the present invention is to eliminate the two drawbacks described above and to provide an image processing device that can reproduce images with high quality and high definition. That is, in the present invention, the moiré phenomenon 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. Moreover, it is possible to realize an even more tedious method with a simple circuit configuration and provide it at a low cost.

<Embodiment> (Basic Configuration) FIGS. 1 to 6 The basic configuration of the image processing apparatus of this embodiment is shown in FIG. This image processing device includes an edge detector a.

It consists of an edge enhancer, a smoother C, and a mixer d. Edge detector a uses characters, line drawings,
The edges of the image are detected, and the halftone dots of the halftone image have a spatial frequency characteristic that prevents them from being detected as edges. The edge enhancer outputs an 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 image are determined to be non-edge areas, and smoothing is performed to average them and prevent moiré. Furthermore, the edges of one image of characters and line drawings are determined to be edge areas, and by emphasizing the edges, halftone dotting of the characters and deterioration of the sharpness of the image are prevented. Furthermore, since the edge area and the non-edge area are continuously connected, no texture change occurs at the boundary.

Next, the principle of this embodiment will be explained from the perspective of frequency characteristics.

First of all, the number of screen lines for the halftone image of the original is usually 1 in black and white.
It ranges from 20 lines to 150 lines, and in color from 133 lines to 175 lines. And moiré is most likely to occur when the screen angle is 0.
This is when the temperature is about 45 degrees. Further, the dot pitch in the main scanning direction during line reading is maximum when it is 45 degrees and the spatial frequency is low, and is minimum when it is 0 degrees and the spatial frequency is high. Table 1 shows the spatial frequencies when the screen angle is 0 degrees and 45 degrees.

Table l The frequency characteristics of such a halftone image have peaks at the fundamental frequency and its high frequencies, as shown in Figure 2a. Also character images,
The frequency characteristics of continuous tone photographic images are shown in Figure 2, c.
become that way.

For such mixed images 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 output dither period.

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. A second-order differential filter has no directionality, but a first-order differential filter has directionality, and is 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 , 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.

Furthermore, as a spatial filter for an edge enhancer, a second-order differential filter, which has no directionality and has a peak at a higher frequency, is better than a first-order differential filter.

The relationship between the frequency characteristics of various spatial filters is calculated using one-dimensional fast Fourier transform (FFT) for the vehicle in front.As an example, the reading sampling interval of the input system is 1ii6mm, and the output system is 16mm. d o ts
/mm and a 4×4 dither matrix is used. The period of the dither pattern is 41/mm in terms of spatial frequency. Also, in reading with l/16mm sampling, 817mm due to the sampling theorem.
It can only detect frequencies up to .

When the matrix size is 5 x 5, the one-dimensional FFT of the second-order differential filter (-1, 0, 2, 0, -1) is shown in Figure 3, and the first-order differential filter (-1, 0, 0, 0, 1) No.1
The dimensional FFT is shown in Figure 4, and another one-dimensional differential filter (-
A one-dimensional FFT of 1°-1,0,1,1) is shown in FIG.

The position of each peak is 41/mm.

2 1/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 l for all the numbers of lines in Table 1, but the second-order differential filter satisfies the condition l for 120 and 133 lines. 4
5'', condition l cannot be satisfied and halftone dots are detected as edges.

Comparing two types of first-order differential filters, the one with a larger pulse width (-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. If you use a first-order differential filter (-1,-1,0,1,1) for edge detection and a second-order differential filter (-1,0°2.0.-1) for edge emphasis, each peak frequency is 2.5 17mm, 4 1/m
Condition 2 is satisfied with m. In other words, edge detection is used to extract a wide area for edge enhancement, and edge enhancement uses a spatial filter that produces sharp edges.

Next, apply a 5×5 smoothing filter (1°1, l
, l, l) is shown in Figure 6. 1
Fundamental frequency of halftone image of 20 lines 45° or more, 3.341
At 1/mm or more, the strength decreases. Also 4×4
When the pitch of the dither matrix is 41/mm, the strength is sufficiently small and Condition 3 is satisfied.

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. In addition, the boundary between the edge area and the non-edge area is continuously connected by changing the mixing ratio in the mixer according to the signal from the edge detector to prevent moiré in the halftone image. This reduces the size of the hardware circuit because it prevents the occurrence of discontinuous texture changes between edge and non-edge areas, and does not require a large matrix size for the spatial filter. It is also advantageous for LSI implementation.

FIG. 7 is a block diagram showing an embodiment of the present invention, where 31 is an input image signal, l is a differential value detection unit that detects the absolute value of the first differential value of the input image signal Sl; handle. S2 is a differential signal connected to the output of the differential value detector 1, 2 is a control signal generator that generates control signals S3 and 54 from the differential signal S2, S3 is the output of the control signal generator 2 and is a control signal, and S4 is also a control signal. The control signal S is output from the control signal generator 2.
3 is a complementary control signal, and 3 is a smoothing processing section for smoothing the input image signal 31, which corresponds to FIG. 1C. S6 is a smoothed image signal smoothed by the smoothing processing unit 3, 4 is a multiplier for calculating 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 image signal −
31 is an edge emphasizing section that emphasizes the edge part, S8 is the edge signal of the edge emphasizing section 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 59, and 510 is a multiplier. The multiplication is the edge signal output from the calculator 6,
7 is an adder for calculating the arithmetic sum of the edge signal 510 and the input image signal S1, an edge emphasizing section 5, and a multiplier 6. Adder 7
This constitutes the edge enhancer shown in FIG.

Sll 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; S12 is the output of the multiplier 8;
9 is an adder that takes the arithmetic sum of output S7 and output S12, S1
3 is the output of the adder 9 and is a processed image signal. Here, the multipliers 4 and 8 and the adder 9 constitute the mixer d in FIG.

In the edge enhancement section 302 corresponding to the edge enhancement device, the output of the edge amount (edge detect) detection section 5 is multiplied by the aforementioned control signal S9 in a multiplier 6. It goes without saying that 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 the pixel of interest and a value multiplied by a certain coefficient of edge detection, and edges of the pixel of interest are emphasized. Mixing section 305
In the following, the output of the edge enhancement unit 302 and the smoothing process 3
A circuit unit that mixes the outputs of
S3. S4 is output. 33. As described below. S4 is a complementary control signal, but is not necessarily limited to 53
．． The characteristics of S4 can be freely selected and set using the control signal S5. The output of the edge emphasis section is multiplied by the multiplier 8 determined by S4;
The outputs of the smoothing processing section are multiplied by S3 as determined by S3, and the outputs of multiplier 4.8 are added together by adder 9, and the block diagram of FIG. 7 can also be expressed by the following equation. can.

First, the differential value detection section 1 and the control signal generator 2 are expressed by the following formula l.
Perform calculations such as.

11---Test 1) Here, 1 is the input image data, and E is the control signal S4.

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 is the output of the adder 7, and kl is a constant of the constant signal S9. Then, the following output is obtained from the smoothing processing section 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.

The part in [ ] shown in the above equation is a kernel that performs convolution with the image signal I. Kernel books 1 to 4 can be modified in various ways, examples of which are shown in Table 2.

*l*2 Tree 3 Book 4 Table 2 FIG. 8 shows an example of the characteristics of the function f of the control signal generating section 2.

E=f(X), E=Offor O<x<0.2 E=1.67 x -0,33 for 0.2<x<0.8 E=1 for 0.8≦X≦1 . However, both input and output signals will be standardized to 0 to 1 in the description.

Figures 9 and 10 show an edge detection section l (
FIG. 2 is a diagram illustrating the operation of the differential value detection section (hereinafter referred to as a differential value detection section), and is shown in one-dimensional main scanning.

As already explained, the differential value detection unit l is a kind of band-pass filter, so that the input image signal Sl, such as the halftone image with high frequency components shown in FIG. If convolution is performed in the main scanning direction with .degree.-1, 0, 1, 1), the output signal S2 will have a small value such as 0.1-0.2.

On the other hand, in the case of a relatively low-frequency input image signal (for example, a vertical line of a character), the output signal S2 takes a large value due to similar convolution, as shown in FIG.

Here, the control signal generating section 2 that performs gamma conversion sets the FHI 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. Further, when the differential signal S2 is larger than 0.8, the control signal S3 is set to O and the control signal S4 is set to 1. Furthermore, the differential signal S2 is 0
．． Between 2 and 0.8, it changes according to the differential signal S2 so that the sum of the control signal S3 and the control signal S4 always becomes 1, as shown in FIG.

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° 1.1), the content is all 1, and it is a low-pass filter configured to output the average of 5 pixels, and the input image signal S1 is smoothed. The image signal becomes as shown in S6.

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) and has the well-known second-order differential edge detection characteristic, and the output S8 has O in the flat part and positive and negative peaks in the edge part. .

Furthermore, the edge signal S8 is multiplied by a constant of 59 by a multiplier 6, and added to the image input signal S1 by an adder 7, thereby producing an edge emphasis signal Sll. Although not shown, since the edge signal SIO 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 SIO, which is input to the adder 7, and the input image signal Sl. It will be done.

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 thicker and S4 is smaller. Further, as described in FIG. 8, 53 and 54 are converted by gun so that their sum always becomes 1. Therefore, multipliers 4 and 8
The sum of the outputs of is the edge emphasis signal S when the differential signal S2 is large.
When the component of ll is large and S2 is small, control is performed so that the component of the smoothed signal S6 is large.

FIG. 13 shows this situation, and shows that the differential signal S2 detects edges in the part of the input image signal S1 excluding short-period vibrations (corresponding to the halftone dot period). .

The control signal S4 is obtained by converting the differential signal S2, and the four non-white parts of the signal S2 shown in FIG. 13 are set to O. S3 is naturally 1-54. Furthermore, FIG. 13 also shows a smoothed signal S6 and an edge emphasis signal S11. In FIG. 13, the processed signal S13 connects S6 and 311 to 5
This is a signal in which the halftone dots added at a ratio of 3 and 54 are smoothed and only the edge portions are emphasized.

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 section l.

The differential value detection unit 1 includes a 5-line eight-way filter 301 shown in FIG.
The output of is input.

In the figure, 306 is a first-order differentiator, and its output 306
-a is a data section 306-c and a sign section 3 indicating positive/negative.
The sign section 306-b is input to the select signal of the selector 308, and the sign section 306-b is input to the select signal of the selector 308, and
The absolute value of the data is 30 by selecting either the +/- inverted data or the data of 306-c.
8-a is obtained. Similarly, the absolute value of the output of the first-order differentiator 112 is output from the selector 311, and the adder 309
308-a and 311-a are added, and the sum of the first-order differential values in two directions is output from the adder 309.

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.

All shift registers in FIG. 15 are shifted in synchronization with an image transfer lock not shown in the figure. To simplify the explanation, all multiplication coefficients of multipliers 243 to 247 are assumed to be 1. According to timing chart Figure 16, t
The output of the shift register 230 at -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 the output of the shift register 232 at -1 is Sn.

m41+Sn, m+Sn, m-1+Sn, m-2, 1
The output of adder 280 at 0 is Sn.

m42+Sn, m41+Sn, m+Sn, m-1+S
n, 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 becomes e*sn,
m42+d*Sn, m41+c*Sn, m+b*Sn,
m-1+a@S n , 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 primary image component to be sought is tree l of formula l.

*In a case like 2, when the pixel of interest is the nth line, n
The kernel elements of line −2 and line n−1 are equal,
Also, the kernel elements of the n+1 line and the n+2 line are equal. Therefore, the image data is obtained by adding the n-2nd line and the n-1st line by the adder 231, and then using the formula 1*l, 1 like tree 2.
The circuit scale can be reduced to 172 by performing the next image processing. The same applies to the (n+1)th line and (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 are as simple as for-1oro, they can be easily constructed with an inverter 291 and a selector 292 as shown in FIG. For-1 can be switched and set to O by CL.

In addition, in order to obtain the linear image component of the kernel *l in Equation 1, the multiplication coefficients of the multipliers 238 to 242 are set to 1, and the multipliers 243 to 2 are
The multiplication coefficient of 47 is set to 0, and the multiplication coefficients of multipliers 248 to 252 are set to -l.

To find the first-order image component of kernel tree 2 of formula l, use 1 multiplier 242.238.239.240.

The multiplication coefficient of 241 is 1.1, 0. -1. -1, and the multiplication coefficients of the multipliers 243 to 247 are 1,1°0, -1. -1
and the multiplication coefficients of the multipliers 248 to 252 are t, i,
Let o, -t, -i.

Also, *1 in Table 2. A circuit configuration for calculating a primary image component like tree 2 can be realized as shown in FIG. 18, but since the operating principle is the same as in FIG. 15, the explanation of how to give coefficients to the multipliers will be omitted.

(Edge emphasis part 5) Next, FIG. 19 shows the edge detection section 5.

As shown in FIG. 20, the input image signal Sl 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 output simultaneously, and in synchronization with an image transfer lock (not shown), the image data is output one pixel at a time in the main scanning direction.

Further, as shown in FIG. 21, the surrounding image data will be considered when the region of interest S in the image area is further enlarged and the pixel data of interest are Sn and m. The edge detection unit 5 in FIG. 19 stores the n-2nd line, n, of the input image data S1.

Image data for three lines of the (n+2)th line is input, and the pixels of interest for image processing are set as Sn and m.

In the figure, 201 to 211 are 1-bit shift registers. Image data Sl is stored in shift registers 201-203.
204 to 208 and 209 to 211 are shifted in synchronization with an image transfer lock (not shown). This timing chart is shown in FIG. 22. In FIG.

The output of the shift register at a certain timing T is the first
9 It is shown enclosed in parentheses in Figure 9.

Adder 213 is shift register 203.204.208
．． 211 output data 5n-2)m, Sn, m-2)S
n, m 42.5n42. m is added, and the added data is multiplied by 11 in a multiplier 214. Pixel of interest Sn
, m are output from the shift register 206 and are output from the multiplier 21
2 is multiplied by 4, added by adder 215, and added by adder 215.
The edge detect signal G·Sll as shown in FIG. 7 is output from the equation 2).

In addition, since the kernel elements of the n-2th line and the n+2th line are the same, shift registers 209 to 211 are omitted and the n-
After adding the outputs of the 2nd line and the (n+2)th line, the output may be input to the shift register 201, or the circuit shown in FIG. 18 may be used.

(Smoothing Processing Unit 3) Next, details of the block of the smoothing processing unit 3 shown in FIG. 7 are shown in FIG. 23.

The image signals S1 each have 5 continuous signals in the sub-scanning direction of the image.
- It consists of line data, and the adder 271 adds 5 pixels in the sub-scanning direction.

This data is delayed by 1 bit in the shift register 27.
2 is input. The output data of shift register 272 is input to adders 277-280. The adder 272 adds the output of the shift register 272 and the data of one pixel before it. The result of this addition is latched into the 274th shift register and then added to the next pixel by the 278th adder. Similarly, at time T2, the adder 280 outputs SN, m+2+sN, m+2+sN as shown in FIG.
, m−+SN.

m-1+sN+m-2 is output (SN, 3 counters)
~Anti.

“5X-2・j+s%-t, j+sIL, J+SI
J ) J Turtle +1. j+S turtle+2.7).

In this way, when the pixel of interest is Sn, m, the pixel summation value shown in tree 3 of Equation 2 is output from the adder 280, and is divided by the number of metalwork pixels by the divider 281 to obtain smoothed data. FIG. 26 shows a circuit that performs weighted smoothing as shown in FIG. 25. Operation timing etc. are second
3, but in FIG. 26 the multipliers 351 to 35
Each line is weighted by 5, and each column is also weighted by a multiplier 3.
Smoothing as shown in FIG. 25 is performed by weighting by 56 to 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 kernel of the differential value detection section, smoothing processing section, and edge enhancement section is 5x5, but it may be 3x3 depending on the number of lines aimed at removing moiré. , 5x5 or more may be required. Also, depending on the purpose, a differential value detection section,
The smoothing processing units do not need to use kernels of the same size, and furthermore, the kernels do not need to be square.

Furthermore, in this embodiment, l! Although it has been described that a 5-line buffer of 1 is provided and edge detection, smoothing, and edge enhancement are performed in parallel processing, it is not necessarily necessary to perform them in parallel.

Further, in this embodiment, the smoothed signal S6 which is the output of the smoothing processing section 3 and the edge emphasis signal 5 which is the output of the adder 7 are
11 is added at a rate according to the output of the gamma conversion unit 2, but the input image signal 31 may be used instead of the edge emphasis signal 311. In this case, the input image signal 31 may be slightly inferior to the present example for characters and line drawings. However, there are advantages in that the apparatus can be greatly simplified and the same effect as the present embodiment can be obtained in terms of suppressing moiré.

Also, the edge emphasis part 5 and the multiplication calculator 6 shown in FIG.
If the edge emphasizing device constituted by the adder 7 and the adder 7 is constructed so that the center of the kernel tree 4 of the edge emphasizing section 5 can be varied by a constant 59, the multiplier 6 and the adder 7J become unnecessary.

Further, although in this embodiment the constant 39 is variable from the outside, 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-C show modified examples of the characteristics of the gamma converter 2. show.

In FIG. 27, only the characteristics of the control signal S4 are shown, but the M control signal S3 is set to 53=1-34.

Figure 27-a is 54=0 where 0<S2<0.5S4=
1. However, it has the characteristic that 0.5<32<1.0, and is particularly characterized in that the circuit of the gamma conversion section can be easily constructed.

FIG. 27 has a characteristic of 54=-arctan (k-52+k), and is particularly characterized in that the connection between the smoothed signal and the edge emphasis signal is smooth.

Figure 27-〇 is 54=0 However, O<S2<0.25S 4 =
0.33 However, 0.25 < S 2 < 0
．． 554 = 0.67 but 0.5 <32
<0.7534 = 1.0 but 0.75<
32<1.0, the circuit is relatively simpler than the embodiment shown in FIG. A characteristic of this method is that the connection between the edge enhancement signal and the edge enhancement signal becomes smooth.

More specifically, for example, Prewitt's edge detection or Sobel's edge detection method, which is well known as a differential value detection section, may be used.

Furthermore, a Laplacian may be used as the edge detection section. Furthermore, the Prewitt edge detection method, the Sobel edge detection method, or the Laplacian method is usually 3
Spatial filter processing is performed using a x3 kernel, but the wood quality of the present invention will not be affected even if the kernel size is expanded to a size other than 3x3.

Can be reproduced.

[Brief explanation of the drawing]

FIG. 1 is a diagram showing the basic concept of an embodiment of the present invention, FIG. 2 is a frequency characteristic diagram of various images, and FIG. 3 is a diagram showing the basic concept of an embodiment of the present invention. 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. FIGS. 9 and 10 are diagrams showing an example of the operation of the differential value detection unit l that performs first-order differentiation, FIG. 11 is a diagram showing an example of the operation of the smoothing processing unit 3, and FIG. A diagram showing an example of the operation of the edge emphasizing section 5, FIG. 13 is a signal waveform diagram of the numbered part in FIG. 7,
FIG. 14 is a detailed circuit diagram of the edge detection section 1, and FIG. 15 is a detailed circuit diagram of the edge detection section 1.
A detailed block diagram of the primary image separator 306 and 312, Fig. 16 is a timing chart showing the operation of the primary image separator, Fig. 17 is an absolute value circuit diagram, and Fig. 18 is a detailed block diagram of the primary image separator. A detailed block diagram of the example, FIG. 19 is a detailed block diagram of the edge emphasis section 5, FIG. 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,
Figure 4 is a diagram showing the operation of the smoothing processing section, Figure 25 is a diagram showing kernels for other smoothing/smoothing processing, and Figure 26 is a diagram showing the operation of the smoothing processing section.
Block diagram for smoothing the figure, Fig. 27(a), (
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, and d
is a mixer, 1 is a differential value detection section, 2 is a control signal generation section, 3
4, 6 and 8 are multipliers, 5 is an edge emphasis section, and 7 and 9 are adders, respectively.

Claims (7)

[Claims] (1) An image signal edge detecting means, a smoothing means for the image signal, an edge emphasizing means for the image signal, and a mixing means for mixing the output of the smoothing means and the output of the edge emphasizing means. . An image processing apparatus, wherein a mixing ratio of the output of the smoothing means and the output of the edge enhancement means is controlled by the output of the edge detection means. (2) The image processing apparatus according to claim 1, wherein the edge detection means performs detection by first-order differentiation. (3) The image processing apparatus according to claim 2, wherein the edge detection means is performed by a sum of first-order differentials in two or more directions. (4) The image processing apparatus according to claim 1, wherein the edge emphasis means is a spatial filter having high-frequency emphasis characteristics. (5) The image processing device according to claim 1, wherein the output of the edge enhancement means is the sum of the image signal and a constant times the output of a spatial filter having high-pass characteristics. (6) The image processing device according to claim 1, wherein the output of the edge enhancement means is the sum of a constant times the output of a spatial filter having bandpass characteristics and the image signal. (7) The image processing apparatus according to claim 1, wherein the edge enhancement means has a spatial filter different from that of the edge detection means.