JPS61157161A - Picture processing device - Google Patents

Abstract

PURPOSE:To reproduce a picture with a high quality and in a high definition by operating a detecting means, a smoothing means, and an emphasizing means by a folding up method and equalizing the size of each nucleus necessary to fold up, concerning the picture processing device of a facsimile equipment, etc. CONSTITUTION:An edge emphasizing device (b) outputs an original picture or an edge emphasizing picture signal where the original picture and an edge are mixed at a certain ratio. A smoothing device (c) smooths a picture. A mixing device (d) changes and outputs the mixing ratio of an edge emphasizing picture and a smoothing picture in accordance with the signal of the edge detecting device. In an edge emphasizing part 302 corresponding to the edge emphasizing device (b), the output of an edge (edge detection) emphasizing part 5 is multiplied with a control signal S9 by a multiplier 6. Further, an edge detecting means (a), a smoothing means (b), and an edge emphasizing means (c) are operated by the folding-up method, and the size of each nucleus necessary to fold up is equalized.

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, uniformity is sampled using a COD sensor, etc.
The digitized data is output from a digital printer such as a laser beam printer to reproduce the image. Due to the development of digital equipment, so-called digital copying devices 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, Φ 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 occurrence factors 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 the halftone frequency fo (= ) determined from the halftone dot pitch P□ (mm) of the halftone original.
(PEL/mm) n times the high frequency clove n f O (PEL/mm) and the input sampling frequency f determined from the input sensor pitch PS (mm)
s (F-T) (PEL/mm) and Δf=Ifs-
nfol (PEL/mm)----1---(1
) A beat frequency occurs, which becomes moiré.

CB) phenomenon, the dither threshold 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 FD (mm) on the recording paper, the spatial frequency is f o = 1/Po (PE L/m
m'), and the beat frequency is Δf = I f Of o I (PEL/mm
)----------1 (2) appears most prominently.

Of the two moiré phenomena mentioned above, the one that occurs most strongly is (B). 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. For example, a random dither method can remove moiré, but graininess appears, resulting in deterioration of image quality.

J, Opt, Soc, Am,, Vol, 66° Nol0
, October 1976 F985. A proposed by Paul G. and Roetling
RIES compares the average value of the density before and after binarization, and applies a feed rate of 98 to the threshold value to make them equal, but this method requires complicated hardening and is not effective in removing moiré. not enough.

On the other hand, the re-halftone method seen in Takashima et al.
By adjusting the halftone image (or averaging the surrounding pixels),
Moiré is removed by redotting with a dither pattern,
There is also less grainy noise.

However, resolution cannot be avoided due to revocability (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 area. For this reason, a so-called image area separation technique is required.

This image area segmentation technology is highly accurate and fast at the current level --- a method that is especially suitable for /\- deware, and is difficult to achieve. . 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 aforementioned fat ting 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, and the mixer d changes the mixing ratio of the edge-enhanced image and the smoothed image according to the signal from the edge detector and outputs the 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, 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, 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. 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 1 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, a first-order differential filter has a peak at a lower frequency than a second-order differential filter. However, the second-order differential filter does not have directionality, but the first-order differential filter has directionality, and is expressed as 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.

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.

To simplify the relationship between the frequency characteristics of the various spatial filters, we show the results calculated using one-dimensional fast Fourier transform (FFT).As an example, the reading sampling interval of the input system is 1 x 16 mm, and the output system is l 6 d o. ts
The calculation is performed using a dither matrix of 4×4/mm. The period of the dither pattern is 417 mm in terms of spatial frequency. Also, in reading with 1x16mm sampling, 81/mm according 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 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 all the numbers of lines in Table 1, but for the second-order differential filter, 45 of the 120 and 133 lines
Condition 1 cannot be satisfied at 100°, 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. Set the edge detection to a first-order differential filter (-1, -1, 0, 1, 1).

Edge enhancement is performed using a second-order differential filter (-1,0°2.0.
-1), each peak frequency is 2.5 1
/mm, 4 1/mm, satisfying condition 2. 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, a 5X5 smoothing filter (l.

The one-dimensional FFT of t, i, t, 1) is shown in Figure 6, and the fundamental frequency of the halftone image of 120 lines of 45° or more,
3.341 The strength decreases at 1/mm or more.

Also, the pitch of the 4×4 dither matrix is 4 1
At 7 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 compensated spheres. Averaging is performed using smoothing, and 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 eliminates the need for a large matrix size for the spatial filter.

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, where Sl is an input image signal and 1 is a differential value detection unit that detects the absolute value of the primary image component value of the input image signal S1, 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 84 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, 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 for calculating the arithmetic product of the smoothed image signal S6 and the control signal S3;
S7 is the output of the multiplier 4, 5 is an edge emphasis unit that emphasizes the edge portion of the input image signal S1, S8 is the edge signal of the edge emphasis unit 5, S9 is a constant given from the outside, and 6 is the edge signal S8. The multiplier that takes the arithmetic product with the constant 59 is a calculator, SI
O multiplies the edge signal output from the calculator 6, 7 an adder that takes the arithmetic sum of the edge signal 510 and the input image signal S1, and the edge emphasis section 5° multiplies by the multiplier 6. The adder 7 constitutes the edge emphasizing device 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 Sll 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, 51
3 is the output of the adder 9 and is a processed image signal. Here, the multiplier 4.8 and the adder 9 constitute the mixer d in FIG.

Here, the multiplication of the edge emphasis device by the corresponding edge emphasis is performed by the calculator 6.
It goes without saying that the control signal S9 is not the multiplication coefficient itself, but may also be a coded signal.

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 S3, a circuit unit mixes the output of the edge enhancement unit 302 and the output of the smoothing processing unit 3 at an appropriate ratio, in accordance with the output of the differential value detection unit 1 input to the control signal generation unit 2 in the previous stage. ．． S4 is output. As described later, 33, S4
are complementary control signals, but are not necessarily limited to,
53. '54 allows its characteristics to be freely selected and set using the control signal S5. The output of the edge emphasis section is multiplied by S4 in the multiplier 8, the output of the smoothing processing section is multiplied by S3 in the multiplier 4, and the output of the multiplier 4.8 is multiplied by the multiplication determined by S3. are added together by the 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 the input image data, and E is the control signal. 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 trees 1 to 4 can be modified in various ways, examples of which are shown in Table 2.

FIG. 8 shows a tree 1 of the function f of the control signal generator 2.
Tree 2 *3 Book 4 Table 2 (for example, vertical lines of letters, etc.) shows E=f shown in Figure 1O.
(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.

FIGS. 9 and 10 show the edge detection unit 1 (
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 of 0.1 to 0.2.

On the other hand, in the case of a relatively low frequency input image signal, it takes 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 O when the differential signal S2 is smaller than 0.2, as shown in FIG. Also, if the differential signal S2 is larger than 0.8, the control signal S3 is set to O,
Set the control signal S4 to 1. Furthermore, the differential signal is 0.2 to 0
．． 8, the control signal S is always maintained as shown in FIG.
3 and the control signal S4 becomes 1, depending on the differential signal S2.

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 Sl is smoothed. The image becomes like the image signal 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 degrees o, -i) and has well-known edge detection characteristics for secondary images, and the output S8 is O in the flat part.

It has positive and negative peaks at the edges.

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 510 is slightly delayed by 4 relative to the input image signal SL, a delay circuit for timing the edge signal SLO, which is the input of the adder 7, and the input image signal SL is actually used. provided.

By the way, when the output of the differential value detector 1 is large, that is, at the edge portion, the control signal S3 is small and S4 is large. Conversely, when the differential signal S2 is small, S3 is thick (S4 is small, and as described in FIG. 8) 53 and 54 are always gamma converted so that their sum 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, the component of smoothed signal S6 is controlled to be large.

FIG. 13 shows this situation, and shows that the differential signal S2 detects edges in a portion of the input image signal 51 excluding short-period vibrations (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 O. S3 is naturally 1-84. Furthermore, FIG. 13 also shows a smoothed signal S6 and an edge emphasis signal SLI. In FIG. 13, the processed signal S13 is connected to S6 and Sll.
This is a signal in which the halftone dots added at a ratio of 3 and S4 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 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. As a result, the absolute value 308-a of the data can be obtained. Similarly, 1
The absolute value of the output of the next image separator 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 primary image separators 306 and 312 shown in FIG. 14 is shown in FIG. 15.

First, in order to explain the basic operation of this primary image dividing circuit, block X in FIG. 15 will be explained.

First, 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.

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

m+2+Sn, m+1+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,
m+2+daS n , m + 1 +
c a S n , m + b @
Sn, m-1+aeSn, 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, when the primary image component to be sought is like tree 1 and tree 2 in Equation 1, and the pixel of interest is the n-th line, the kernel elements of the n-2 line and the n-1 line are equal, and the n+1 The kernel elements of the line and the n+2 line are also equal, so the image data is sent to the adder 23 for the n-2nd line and the n-1st line.
The circuit scale can be reduced to 1/2 by performing addition by 1 and then performing linear image processing such as tree l and tree 2 in equations 1 and 2. Also, n
The same applies to the +1st line and the n+2nd 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 first-order image component of kernel part 1 of equation 1, the multiplication coefficients of multipliers 238 to 242 are set to 1, and the multipliers of 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 obtain the first-order image component of the kernel 2 of formula l, use the 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 set to 1°1.0. -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 such as trees 1 and 2 in Table 2 can be realized using FIG. 18, but since the operating principle is the same as in FIG. is omitted.

(Edge emphasis part 5) Next, FIG. 19 shows the edge emphasizing 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 emphasizing unit 5 in FIG. 19 stores the (n-2)th line and n of the input image data Sl.

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.0 image data Sl is in shift registers 201 to 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. 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, 204, 208
, 211 output data 5n-2,m, Sn,m-2,S
n, m+2, and S n + 2 + m are added, and the added data is multiplied by 1 in the multiplier 214. Featured pixel S
n and m are output from the shift register 2.06, multiplied by 4 by the multiplier 212, added by the adder 215, and then added to the adder 2.
15 outputs the edge detect signal G.Sll as shown in equation 2 and FIG.

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+2nd line, it may be input to the shift register 201, or by inputting the value of the kernel 4 to the multiplier using the circuit shown in FIG. It is also clear that it is possible to construct

(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 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 SN, m+2+sH, m+1+s as shown in FIG.
N, m+sN.

m + l + S H, m -2 is output (SN
, j=Sn-2, j+5n-1, j+Sn, j+sn+
1・j+S n + 213) *Thus, 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 primes by the divider 281 to obtain smoothed data. is obtained. In addition, the 26th circuit is a weighted smoothing circuit as shown in FIG.
It is a diagram. The operation timing etc. are the same as in FIG. 23, but in FIG. 26, each line is weighted by multipliers 351 to 355, and multipliers 356 to 3601 . : Smoothing as shown in FIG. 25 is performed by giving more weight.

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 Embodiments) In this embodiment, the kernels of the differential value detection section, smoothing processing section, and edge enhancement section were set to 5×5, but depending on the number of lines aimed at removing moiré, it may be 3×3 or 5×5. ×5 or more may be required.

Further, in this embodiment, the smoothed signal S6 which is the output of the smoothing processing section 3 and the edge emphasis signal S which is the output of the adder 7 are
Although ll is added at a rate according to the output of the gamma conversion unit 2, the input image signal St may be used instead of the edge emphasis signal Stt. However, the apparatus can be greatly simplified, and in addition to suppressing moiré, if the concentric part is configured to be variable by a constant 89 as in this embodiment, the multiplier 6 and the adder 7 are not necessary.

Further, in this embodiment, the constant 89 is variable from the outside, but it may be fixed internally.

In addition, in the embodiment of the present invention, the characteristics of the control signal generator 2 that performs gamma conversion are explained as shown in FIG. 8, but the characteristics of the gamma converter 2 are shown in FIGS. A modified example is shown.

In FIG. 27, only the characteristics of the control signal S4 are shown, but the control signal S3 is expressed as 53=l-54.

Figure 27-a is 54=0 where 0 <S2<0.5S 4
= 1.0 ta: L O,5< S 2 <
It has a characteristic of 1.0, and is particularly characterized in that the circuit of the gamma conversion section can be easily configured.

FIG. 27 has the characteristic that 54=-arct an (k*s2+k), and is particularly characterized in that the connection between the smoothed signal and the edge emphasis signal becomes smooth.

Figure 27-C is 54=0 1? ,”L O<S2<0.25S4
;0.33 Just L O, 25<S2<0.5S
4 = o, 67 ta? , : I, 0.5
< S 2 (0,75S4=1.OtadaL O,7
5<S2<1.0, and compared to the embodiment shown in FIG. 8, the circuit is relatively simple and the circuit shown in FIG.
The characteristic of this method is that the connection between the smoothed signal and the edge-enhanced signal becomes smoother than when the characteristics of the gamma conversion section shown in FIG.

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, the Laplacian may be used as the edge detection section, and the Prewitt edge detection method, the Sobel edge detection method, or the Laplacian is usually
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.

Effects As described above, according to the present invention, moiré can be prevented from occurring when a halftone image is reproduced, and characters and thin lines can be faithfully reproduced by edge enhancement when a halftone image is reproduced. 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 lines/heteroposteriors can be made equal and the same. A line buffer can be used and the circuit scale can be designed to be small.

[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. 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. Fig. 9 is a gamma conversion characteristic diagram of . FIG. 1O is a diagram showing an example of the operation of the differential value detection unit that performs first-order differentiation, FIG. 11 is a diagram showing an example of the operation of the smoothing processing unit 3, and FIG. 12 is a diagram showing an example of the operation of the edge detection unit 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 l, FIG. 15 is a detailed block diagram of the first-order differentiators 306 and 312, and FIG. 16 is a first-order differentiator. 17 is an absolute value circuit diagram, FIG. 18 is a detailed block diagram of another example of the first-order differentiator, FIG. 19 is a detailed block diagram of the edge emphasis section 5, and FIG. 20 is a buffer diagram. Circuit diagram, FIG. 21 is a diagram showing the image area, FIG. 22 is a diagram showing the operation of the edge emphasis section, and FIG. 23 is a diagram showing the smoothing processing section 3.
FIG. 24 is a diagram showing the operation of the smoothing processing section, FIG. 25 is a diagram showing the kernel for other smoothing processing, and FIG. 26 is a diagram showing the kernel for performing the smoothing process in FIG. 25. The block diagram and FIGS. 27(a), 27(b), and 27(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, and C is an edge detector.
is a smoother and 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 is a multiplier, 5 is an edge emphasis section, 7,
9 indicates a kaginoki. Figure 7 Figure 2 F (I /#T#) Ll/MMJ (1 voice) Figure 71 Figure 27 (Q) cb) (C')

Claims (1)

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