JPS61157164A - Picture processing device - Google Patents

Abstract

PURPOSE:To reproduce a picture with a high quality and in a high definition by making variable the mixing rate of a mixing means by the output of an edge detecting means and constituting the edge detecting means with the primary differential device, concerning the picture processing device of a copying machine, a facsimile equipment, etc. CONSTITUTION:In a mixing part 305, at the circuit part to mix an input picture and the output of a smoothing processing 3 by the suitable rate, S3 and S4 are outputted in accordance with the output of a differential value detecting part 1 inputted in a control signal generating part 2. As mentioned later, the S3 and S4 are complementary control signals and not always limited. The S3 and S4 can freely select and set the characteristic by a control signal S5. In a multiplier 8, the input picture executes the multiplying determined by the S4, in a multiplier 4, a smoothing processing part output executes the multiplying determined by the S3, the output of the multipliers 4 and 8 is mutually added by an adder 9, and this goes to be a picture processing output.

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, this method has the following two 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 the moiré phenomenon, and the cause of its occurrence is as follows.
- A) Moiré caused by the N-point original and input sampling; B) Moiré caused by the halftone original and the dither threshold matrix. The phenomenon of A) is the halftone dot frequency 1O (= ) determined from the halftone dot pitch P□ (mm) of the halftone original.
(PEL/mm) From the n-times harmonic clove nfO (PEL/mm) and the input sampling frequency fS (= ) (PEL/mm) found from the input sensor m-pitch PS (mm), Δf
= IJ' 5-nfOl (PEL/mm) ---
-1---(1) A beat frequency occurs, which becomes moiré.

In the phenomenon (B), the dark value of dither is generally 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 Pp (mm) on the recording paper, the spatial frequency is i o = l/P o (PEL/
mm), and the most prominent beat frequency is Δf=l/'0-fol (PEL/mm).

Of the two moiré phenomena mentioned above, the one that occurs most strongly is CB). In the case of phenomenon (A), this is generally in the order of n = 3 to 6 as n of the n-th harmonic of the halftone original, and the transfer function (MTF) of the optical system etc. 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, such as the random dither method, which can remove moiré, but produces grainy graininess and deteriorates the image quality.

J, Opt, Soc, Am,, Vol, 66.

Nol0,0ctober 1976 P98
ARIES proposed by Paul G. Roetling, shown in 5, compares the average value of the density before and after binarization and applies feedback to the dark value so that it becomes equal. Hardening is complicated and moiré removal effect is not sufficient.

On the other hand, the re-halftone method seen in Takashima et al.'s Proceedings of the Institute of Image Electronics Engineers 83-3 PI3 "Half-dot conversion of mixed text/photo images" creates a dither pattern by poking the half-tone image (or averaging the surrounding pixels). Moiré is removed and grainy noise is also reduced.

However, resolution cannot be avoided due to pokashi (or averaging with surrounding pixels).In other words, if you try to remove moire, the resolution will decrease, and if you try to maintain resolution, moire will not be removed.Therefore, only the halftone image area It is essential to extract it 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 segmentation technology is a highly accurate and fast method at the current level - a method particularly suitable for hardware implementation.
However, even if image area segmentation technology were to be obtained, such a method would average and smooth even the high-frequency components within the image, and would not be fully satisfactory. do not have.

On the other hand, regarding problem (2), the characters and line drawings on the original are shredded by dithering, and the print quality deteriorates because the push portions in particular are cut off. 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. Furthermore, this method can be realized with a simple circuit configuration and can be provided at low cost.

<Example> (Basic configuration) The basic configuration of the image processing apparatus of this example is shown in FIG. 1. The zero image processing apparatus has an edge detector a.

It consists of a smoother C9 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 smoother C smooths the image, and the mixer d changes the mixing ratio of the input image and the smoothed image according to the signal from the edge detector a and outputs the mixed image. 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, since the edge area and the non-edge area are continuously connected, no texture changes occur 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. Moiré is more likely to occur when the screen angle is 0.
This is when the temperature is about 45 degrees. In addition, the halftone dot pitch in the main scanning direction during line reading is maximum when the screen angle is 45 degrees, and the spatial frequency is low, and when it is 0 degrees, the minimum spatial frequency is high, and the spatial frequency is when the screen angle is 0 degrees and 45 degrees. The results obtained are as shown in Table 1.

Table 1 The frequency characteristics of such a halftone image have peaks at the fundamental frequency and its harmonics as shown in Figure 2a.
The frequency characteristics of continuous tone photographic images are shown in Figure 2, c.
become that way. For such a mixed image of characters, photographs, and halftone dots, the spatial filter of the edge detector and smoother of this embodiment has a frequency characteristic that satisfies 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 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, a first-order differential filter has a peak at a lower frequency than a second-order differential filter. However, the second-order differential filter has no directionality, but the first-order differential filter has directionality, at least? It is necessary to take the square root of the sum of the squares of the slopes in the directions, or as an approximate expression thereof, the sum of the absolute values of the slopes in at least two directions, 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.

For simplicity, the relationship between the frequency characteristics of the various spatial filters as described above is calculated using one-dimensional fast Fourier transform (FFT). For example, the reading sampling interval of the input system is 1/16 mm, and the output system is 16 dots/mm.
Calculation is performed using a ×4 dither matrix. The period of the dither pattern is 4 when converted into spatial frequency.
It is 17mm. Further, in reading with 1/16 mm sampling, only frequencies up to 817 mm can be detected due to the sampling theorem.

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 first-order differential filter (-1
A one-dimensional FFT of ゜-1, 0, 1, 1) is shown in Fig. 5.

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 the 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
Comparing two types of first-order differential filters that fail to satisfy condition 1 at 5° and detect halftone dots as edges, we found that the pulse width was increased (-1, -1, 0°■, 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 can be detected.

Next, a 5×5 smoothing filter (1°1, 1
, l, 1) is shown in Figure 6, 12
Fundamental frequency of halftone image of 0 line 45° or more, 3.341
The strength decreases when the diameter is 17 mm or more. Further, the strength is sufficiently small when the pitch of the dither matrix is 417 mm in 4×4, and Condition 3 is satisfied.

In this embodiment, by using a spatial filter with frequency characteristics such as the conditions 1 and 2 described above for the edge detector and smoother, the flat part of the image and the halftone image are determined to be non-edge areas, and are averaged by smoothing. Text, line drawings, and edges of images are output as is. 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 S is an input image signal and l is a differential value detection unit that detects the absolute value of the first differential value of the input image signal Sl, which corresponds to FIG. 1a. do. 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, S4
1 is also an output of the control signal generator 2 and is a control signal complementary to the control signal S3. 3 is a smoothing processing section that detects the edge portion of the input image signal St, and corresponds to FIG. 1C. 36 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; 8 is the input image signal S
The multiplier that takes the arithmetic product of l and the control signal S4 is a multiplier, S12 is multiplied by the output of the multiplier 8, 9 is an adder that takes the arithmetic sum of the output S7 and the output S12, and S13 is processed by the output of the adder 9. It is an image signal. Here, the multiplier 4.8 and the adder 9 constitute the mixer d in FIG.

The mixing unit 305 is a circuit unit that mixes the input image and the output of the smoothing process 3 at an appropriate ratio.
3 and 34 are output. As described later, S3. Although S4 is a complementary control signal, it is not necessarily limited to this, and the characteristics of S3 and S4 can be freely selected and set using the control signal S5. The input image is S4 in the multiplication calculator 8.
The multiplier 4 performs the multiplication determined by S3, and the smoothing processing unit output is multiplied by the multiplier 4 determined by S3.
The outputs of the multipliers 4.8 are added together by an 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 unit l and the control signal generator 2 are expressed by the following formula l
Perform calculations such as.

Here, I is input image data and E is control signal S4.

5 is a normalization function that normalizes the control signal S4 to a maximum value of 1.

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 O of the output 313 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.

*1 to Book 3 may have various modifications, examples of which are shown in Table 2.

Tree 1 Tree 2 Tree 3 Table 2 FIG. 8 shows an example of the characteristics of the function f of the control signal generator 2.

E=f (x), E=Of for 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 1 is a kind of band-pass filter, so that the input image signal S1, such as the halftone image having 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 control signal S3 to 1 and sets the control signal 'S4 to O 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, the differential signal is 0.2 ~
0.8, the differential signal S2 is set so that the sum of the control signal S3 and the control signal S4 always becomes 1 as shown in FIG.
It changes depending on.

On the other hand, the input image signal 51 is connected to the input of the differential value detection section 1, and is also connected to the smoothing processing section 3 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°l, 1), the contents are all 1, and it is a low-pass filter configured to output the average of 5 pixels, and the input image signal SL is a smoothed image signal. It will look like S6.

By the way, when the output of the differential value detection section l 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 thick and 34 is small, and as described in FIG. In addition, S3 and 34 are gamma-converted so that their sum always becomes 1. Therefore, multipliers 4 and 8
The sum of the outputs is controlled so that when the differential signal S2 is large, the component of the input image S1 is large, and when S2 is small, the component of the smoothed signal S6 is large.

FIG. 12 shows this situation, and shows that the differential signal S2 detects edges in a portion of the input image signal St excluding short-period vibrations (corresponding to the halftone dot period).

The control signal S4 is a gamma-converted version of the differential signal S2, and all peaks other than the four peaks of the signal S2 shown in FIG. 12 are set to O. S3 is naturally 1-34. Furthermore, FIG. 12 also shows the smoothed signal S6. In FIG. 12, the processed signal S13 is a signal in which the edge portions of the halftone dot portions added at the ratio of S6 and 51t-53 and 54 are detected and only the edge portions are emphasized.

(Description of each part) The input image signal Sl shown in FIG. 7 consists of data for five consecutive lines of image data, as shown in FIG. After being stored at , five lines are simultaneously output, and in synchronization with an image transfer lock (not shown), the image data is output pixel by pixel in the main scanning direction.

Furthermore, as shown in FIG. 19, the region of interest S in the image area is further enlarged, and surrounding image data will be considered when the pixel data of interest is Smm.

(Differential value detection unit l) FIG. 13 is a detailed circuit diagram of the differential value detection section l.

The differential value detection section l has a 5-line buffer 301 shown in FIG.
The output of is input.

In the figure, 306 is a first-order differentiator, and its output 306
-a is the data section 306-c.

The sign section 306-b is inputted to the select signal of the selector 308, and selects either the data inverted by + or - by the inverter 307 or the data of 306-c. The absolute value of data 30g-a is obtained from the back. Similarly, 1
The absolute value of the output of the 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.

Next, a block diagram showing details of the first-order differentiators 306 and 312 shown in FIG. 13 is shown in FIG. 14.

First, in order to explain the basic operation of this primary image dividing circuit, block X in FIG. 14 will be explained.

First, all shift registers in FIG. 14 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 15, 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 7-1 is Sn.

m+1+Sn, m+Sn, m-1+Sn, m-2, t
The output of adder 260 at o is Sn.

m42+Sn, m+1+Sn, m+Sn, m-1+3n
, m-2. In this way, the sum of five pixels in the main scanning direction is calculated in block X. Here, the multiplier 243
-247 multiplication coefficients are set to a, b, c, d, e, the adder 280 (7) output is e*Sn, m42
+d*Sn, m41+c*Sn, m+1+Sn, m-1
+a*Sn, m-2 and 6* It can 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 the same, so the image data is added by the adder 231 to the n-2th line and the n-1th line, and then converted to 1 as in Equation 1 tree l9 lines 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. It can be switched 1 or -1 and set to O by CL.

Further, in order to obtain the first-order image component of the kernel tree l of the formula l, the multiplication coefficients of the multipliers 238 to 242 are set to 1.

The multiplication coefficients of the multipliers 243 to 247 are O, and the multiplier 24
Let the multiplication coefficient of 8 to 252 be -l.

To obtain the first-order image component of kernel zero 2 in Equation 1, multiplier 2
42.238.239.240.

The multiplication coefficient of 241 is 1.1, 0. -1. -1, and the multiplication coefficients of the multipliers 243 to 247 are 1°i, 0. -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 primary image components like Book 1 and Tree 2 in Table 1 and Tree 2 can be realized using Figure 17, but since the operating principle is the same as in Figure 14, the method of giving coefficients to the multiplier is is omitted.

(Smoothing Processing Unit 3) Next, details of the blocks of the smoothing processing unit 3 shown in FIG. 7 are shown in FIG. 20.

The image signals St are 5 consecutive in the sub-scanning direction of the image.
It consists of line data, and is processed by an adder 271 for sub-scanning 5
Pixel addition is performed.

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 Ss+m+2+Ss+m+t+SN as shown in FIG.
+m+SN+m-1+SN,m-2 are output (SN
,j=SN-2,j+5N-1,3+sN,j+SN
+1. j+sN+2.4).

In this way, when the pixel of interest is Sn, m, the pixel sum indicated by zero 3 in equation 2 is output from the adder 280 and is then input to the divider 28.
Smoothed data is obtained by dividing the total prime number by 1. Further, FIG. 23 shows a circuit that performs weighted smoothing as shown in the 22nd row. The operation timing etc. is the 20th
Although it is the same as the figure, the multipliers 351 to 355 in FIG.
Each line is weighted by a multiplier 356 and each column is also weighted by
Smoothing as in 221g is performed by weighting by .about.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 unit and the smoothing processing unit is 5×5, but depending on the number of lines aimed at removing moiré, it may be 3×3 or 5×5. More than this may be necessary in some cases. Furthermore, depending on the purpose, the differential value detection section and the smoothing processing section do not need to use kernels of the same size, and furthermore, the kernels do not need to be square.

Further, in this embodiment, a set of 5-line buffers is provided and edge detection, smoothing, and edge enhancement are performed in parallel processing, but 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 thick section 3, and the input image signal Sl are added at a ratio according to the output of the gamma conversion section 2, but the input image signal sl is added instead of Sl. A signal with edge enhancement may also be used.

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. 24-a-C show modified examples of the characteristics of the gamma converter 2. show.

In FIG. 24, only the characteristics of the control signal s4 are shown, but the control signal s3 is set to be 53=1-54.

Figure 24-a is 54=0 ta? ,”L, O<52<0.5S4
=1.0 TADALO,5<32<1.0, and has the characteristic that the circuit of the gamma conversion section can be easily constructed.

FIG. 24 has the characteristic that 54=-ar'ct an (k-52+k), and is particularly characterized in that the connection between the smooth signal and the edge emphasis signal becomes smooth.

Figure 24-C is 54=0? , dashi 0 <32<0.25
34=0.33 Just L O, 25<S2<0.5
S4=0.67 Just I, o, 5 <52<o
, 75S4=1.0 Ta! , 0.75<32
<1.0, and for the embodiment shown in FIG.
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. 24-a are used.

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

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 can be applied even if the kernel size is expanded to a size other than 3x3. It has no impact.

Effects> As explained above, according to the present invention, it is possible to separate a flat halftone dot part from an image, and since the edge part of the halftone dot part is detected, it is possible to suppress moiré caused by halftone dots and dither patterns.

Furthermore, since characters and thin lines are not smoothed, the original image can be faithfully reproduced.

Furthermore, it does not affect non-halftone images such as photographic images.

[Brief explanation of drawings]

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. Figures 4 and 5 are frequency characteristic diagrams of various differential filters, Figure 6 is a frequency characteristic diagram of a smoothing filter, Figure 7 is a diagram of the image processing block of this embodiment, and Figure 8 is a control signal generation diagram. 9 and 10 are diagrams showing an example of the operation of the differential value detection unit l that performs first-order differentiation, and FIG. 11 is a diagram showing an example of the operation of the smoothing processing unit 3. Figure 12 is a signal waveform diagram of the numbered part in Figure 7, Figure 13 is a detailed circuit diagram of the edge detection unit l, and Figure 1
4 is a detailed block diagram of the first-order differentiators 306 and 312, and FIG. 15 is a timing chart showing the operation of the first-order differentiator.
Figure 16 is an absolute value circuit diagram, Figure 17 is a detailed block diagram of another example of a first-order differentiator, Figure 18 is a buffer circuit diagram, Figure 19 is a diagram showing the image area, and 20th rgJ is a smoothing diagram. FIG. 21 is a detailed block diagram of the processing section 3, FIG. 21 is a diagram showing the operation of the smoothing processing section, and FIG. 22 is a diagram showing other gamma conversion characteristics. In the figure, a is an edge detector, C is a smoother, d is a mixer, l is a differential value detection section, 2 is a control signal generation section, 3 is a smoothing processing section, and 4
and 6 and 8 indicate multipliers, respectively.

Claims (1)

[Claims] 1. An edge detection means for detecting an edge portion of an image signal, a smoothing means for smoothing the image signal, and a mixing means for mixing the smoothing means and the image signal. ,
An image processing apparatus characterized in that the mixing ratio of the mixing means is varied depending on the output of the edge detection means. 2. The image processing apparatus according to claim 1, wherein the edge detection means is constituted by a first-order differentiator. 3. The image processing apparatus according to claim 2, wherein the edge detection means obtains a sum of first-order differentials in two or more directions.