CN110072034B - Image processing method and image processing device - Google Patents

Abstract

The invention discloses an image processing method and an image processing device, which are used for filtering images. The image processing method includes the following steps: (A) generating a weight for each reference pixel, wherein the image includes a plurality of reference pixels, and the size of the weight is between a target pixel and the reference pixel corresponding to the weight and (B) performing a wireless impulse response filtering operation according to the weights, the pixel values of the target pixel and the pixel values of the reference pixels to obtain a wireless impulse response filtering value of the target pixel.

Description

Image processing method and image processing device

technical field

The present invention relates to image processing, in particular to an image processing method and an image processing device based on infinite impulse response (IIR).

Background technique

FIG. 1 is a schematic diagram of a window for image processing. The 5×5 window (including 25 pixels) in the figure is used to perform image processing on the target pixel P(i,j) in the center, and other pixels in the window are reference pixels. Assuming that the infinite impulse response (IIR) filtering operation in the spatial domain is performed, the image is processed in the scan order (Raster scan) from the upper left to the lower right, so the pixels in the white part of the window are untouched. The original pixel processed by IIR filtering has the original pixel value PV _ORI , and the gray part of the pixel is the pixel that has been previously processed by IIR filtering (referred to as IIR pixel), and its pixel value is the IIR filtering value PV _IIR . In detail, the reference pixel P(i-2,j-2) is an IIR pixel (the pixel value is PV _IIR (i-2,j-2)), and the reference pixel P(i-2,j-1) is an IIR Pixels (the pixel value is PV _IIR (i-2,j-1)), ...; the target pixel P(i,j) is the original pixel (the pixel value is PV _ORI (i,j)), the reference pixel P(i, j+1) is the original pixel (the pixel value is PV _ORI (i,j+1)), the pixel P(i,j+2) is the original pixel (the pixel value is PV _ORI (i,j+2)),… .

IIR filtering is to average the IIR pixels in the window and the target pixels to filter the target pixels. Taking the example of Figure 1, the IIR filtering is to obtain the IIR filtering value of the target pixel P(i, j) according to the formula (1). PV _IIR (i,j), that is, calculate the arithmetic mean of the pixel value PV _IIR of the 12 IIR pixels and the pixel value PV _ORI (i,j) of the target pixel P(i,j):

PV _IIR (i,j)=(PV _IIR (i-2,j-2)+PV _IIR (i-2,j-1)+PV _IIR (i-2,j)+PV _IIR (i-2, j+1)+PV _IIR (i-2,j+2)+PV _IIR (i-1,j-2)+PV _IIR (i-1,j-1)+PV _IIR (i-1,j) +PV _IIR (i-1,j+1)+PV _IIR (i-1,j+2)+PV _IIR (i,j-2)+PV _IIR (i,j-1)+PV _ORI (i,j-1) j))/13 (1)

After the calculation of formula (1) is completed, the target pixel P(i,j) becomes an IIR pixel, and becomes the reference pixel in the next round of IIR filtering (taking the pixel P(i,j+1) in Figure 1 as the target pixel) . Because the pixels referenced by IIR filtering are IIR pixels rather than original pixels, and IIR pixels are less susceptible to noise interference than original pixels, IIR pixels, so IIR filtering can use fewer reference pixels (meaning that hardware resources can be saved) Do effective low-pass filtering to remove high-frequency noise in the image. However, taking the scanning sequence from upper left to lower right as an example, IIR filtering only takes pixels to the left, upper left, upper and/or upper right of the target pixel as a reference, and does not consider pixels in other directions, so it is easy to cause The image dragging phenomenon in which the pixel features on the left, upper left, upper, and/or upper right spread to other directions. This image dragging phenomenon is easy to cause color distortion, and if it occurs at the edge of the image, the image edge will be blurred. Therefore, how to effectively remove high-frequency noise and avoid image dragging and color distortion is an important issue in the field.

SUMMARY OF THE INVENTION

In view of the deficiencies of the prior art, an object of the present invention is to provide an image processing method and an image processing apparatus.

The present invention discloses an image processing method for filtering an image, including the following steps: (A) generating a first weight for each first reference pixel, wherein the image includes a plurality of first reference pixels, and The size of the first weight is related to the similarity between a target pixel and the first reference pixel corresponding to the first weight; (B) according to the first weights, the pixel value of the target pixel and the first Calculate a first filter value of the target pixel from the pixel value of a reference pixel; (C) generate a second weight for each second reference pixel, wherein the image includes a plurality of second reference pixels, and the second weight The size is related to the similarity between the target pixel and the second reference pixel corresponding to the second weight; and (D) according to the second weight, the first filter value of the target pixel and the first filter value A second filter value of the target pixel is calculated from the pixel values of the two reference pixels.

The present invention further discloses an image processing apparatus for filtering an image, comprising a first weight calculation circuit, a first filter value calculation circuit, a second weight calculation circuit and a second filter value calculation circuit. The first weight calculation circuit generates a first weight for each first reference pixel, wherein the image includes a plurality of first reference pixels, and the size of the first weight corresponds to a target pixel and the first weight The similarity between the first reference pixels is related. The first filter value calculation circuit calculates a first filter value of the target pixel according to the first weights, the pixel values of the target pixel and the pixel values of the first reference pixels. The second weight calculation circuit generates a second weight for each second reference pixel, wherein the image includes a plurality of second reference pixels, and the size of the second weight corresponds to the target pixel and the second weight. The similarity between the second reference pixels is related. The second filter value calculation circuit calculates a second filter value of the target pixel according to the second weights, the first filter value of the target pixel and the pixel values of the second reference pixels.

The present invention further discloses an image processing method for filtering an image, including the following steps: (A) generating a weight for each reference pixel, wherein the image includes a plurality of reference pixels, and the size of the weight is the same as that of the reference pixel. The similarity between a target pixel and the reference pixel corresponding to the weight is related; and (B) performing an infinite impulse response filtering operation according to the weight, the pixel value of the target pixel and the pixel value of the reference pixel, to obtain an infinite impulse response filter value of the target pixel.

The image processing method and the image processing apparatus of the present invention implement an edge protection mechanism to suppress the image dragging phenomenon of IIR filtering. Compared with the conventional technology, the present invention can protect the edge structure of the image while filtering.

With regard to the features, implementations and effects of the present invention, embodiments are described in detail as follows in conjunction with the drawings.

Description of drawings

[FIG. 1] is a schematic diagram of a window for image processing;

2 is a functional block diagram of an embodiment of the image processing apparatus of the present invention;

3A-3B is a flowchart of an embodiment of an image processing method of the present invention;

[FIG. 4] is a schematic diagram of another window for image processing;

[ Fig. 5 ] is a functional block diagram of an embodiment of a weight calculation circuit;

[FIG. 6A-6B] is one example of the relationship diagram of weight and pixel difference;

FIG. 7 is a functional block diagram of another embodiment of the image processing apparatus of the present invention;

[ FIG. 8 ] is a flowchart of another embodiment of the image processing method of the present invention; and

[ FIG. 9 ] is a schematic diagram of another window for image processing.

【Symbol Description】

200 Image processing device

210, 710 memory

220, 720, 740 weight calculation circuit

230, 730, 750 filter circuit

510 pixel similarity calculation circuit

520 Weight Decision Circuit

S310～S340, S810～S860 Steps

Detailed ways

The technical terms used in the following description refer to the common terms in the technical field. If some terms are described or defined in this specification, the interpretation of this part of terms shall be subject to the descriptions or definitions in this specification.

The disclosure of the present invention includes an image processing method and an image processing apparatus. Since some elements included in the image processing apparatus of the present invention may be known elements individually, the details of the known elements will be omitted in the following description without affecting the full disclosure and practicability of the invention of the apparatus. .

FIG. 2 is a functional block diagram of an embodiment of an image processing apparatus of the present invention, and FIG. 3 is a flowchart corresponding to the image processing method of FIG. 2 . The image processing apparatus 200 includes a memory 210 , a weight calculation circuit 220 and a filter circuit 230 . When performing image processing, the image processing apparatus 200 first obtains a target pixel P(i,j), and uses several pixels adjacent to the target pixel P(i,j) as reference pixels (step S310 ), wherein the reference pixels are IIR pixels. Taking FIG. 1 as an example, the reference pixel may include the 12 IIR pixels, and the memory 210 needs to store at least the 12 IIR pixels. FIG. 4 shows another embodiment of a window used by the image processing apparatus 200 . In this example, the reference pixel only includes two IIR pixels, and the memory 210 needs to store at least the two IIR pixels. Compared with FIG. 1 , the embodiment of FIG. 4 can store at least 10 less pixel values of reference pixels.

Next, the weight calculation circuit 220 generates a weight for each reference pixel (step S320). More specifically, the weight calculation circuit 220 obtains the reference pixel from the memory 210, and calculates each reference pixel according to the similarity between the reference pixel and the target pixel. Each reference pixel determines a weight W, that is, the weight W is related to the similarity between the two pixels. FIG. 5 is a functional block diagram of an embodiment of the weight calculation circuit 220 . First, the pixel similarity calculation circuit 510 calculates the similarity between each reference pixel and the target pixel according to the pixel value PV _tar of the target pixel and the pixel value PV _ref of the reference pixel to obtain a difference d (step S322 ). For example, the pixel similarity calculation circuit 510 calculates the absolute difference d=|PV _tar -PV _ref |, the larger the absolute difference d, the lower the similarity between the target pixel and the reference pixel. In this embodiment, the pixel value PV _tar is the original pixel value PV _ORI (i, j) of the target pixel P(i, j), and the pixel value PV _ref is the IIR filtered value PV _IIR of the reference pixel.

Then, the weight determination circuit 520 determines a weight W for each reference pixel according to the difference d (equivalent to the similarity of the pixels), the maximum weight Wmax, the first threshold th1 and the first threshold th2 (step S324). 6A and 6B are one example of a relationship diagram of weights and differences d. When the difference value d is smaller than the first threshold value th1, it means that the similarity between the target pixel and the reference pixel is high, and the weight determination circuit 520 gives the reference pixel the highest weight Wmax; when the difference value d is between the first threshold value th1 and the second threshold value th2, the weight W decreases with the increase of the difference value d; when the difference value d is greater than the second threshold value th2, the similarity between the target pixel and the reference pixel is low, and the weight is determined at this time. Circuit 520 assigns the reference pixel a weight W of zero. For the convenience of hardware implementation, the weight determination circuit 520 can automatically divide the first threshold value th1 and the first threshold value th2 into q equal parts (q is an integer greater than 1, in the example of FIG. 6B q =3), making the weight W present a stepwise change. Compared to the implementation of FIG. 6A , FIG. 6B can omit the divider for calculating the slope between the thresholds th1 and th2 of FIG. 6A .

After obtaining the weight of each reference pixel, the filtering circuit 230 calculates the weighted average of the target pixel and the reference pixel according to the formula (2) to obtain the IIR filter value of the target pixel (step S330 ).

PV _IIR (i, j)=((W _SUM -W _NBR )×PV _ORI (i, j)+∑(W×PV _IIR ))/W _SUM (2)

As shown in FIG. 3B , step S330 can be subdivided into calculating a plurality of first products (W×PV _IIR ) (step S332 ), calculating a second product ((W _SUM −W _NBR )×PV _ORI (i,j) ) (step S334 ), and obtain the filter value of the target pixel according to the first products and the second products (step S336 ). Specifically, step S336 is to divide the sum of the first products and the second products by the user-defined weight sum W _SUM to obtain the filter value of the target pixel. Among them, W _NBR is the sum of the weights of all reference pixels, the first product (W×PV _IIR ) is the product of the weight W of a reference pixel and the IIR filter value of the reference pixel, ∑(W×PV _IIR ) is all The sum of the first products of the reference pixels of . For example, taking Fig. 4 as an example, the formula (2) becomes:

PV _IIR (i,j)=((W _SUM -W _NBR )×PV _ORI (i,j)+W(i-1,j)×PV _IIR (i-1,j)+W(i,j- 1)×PV _IIR (i,j-1))/W _SUM (3)

in,

W _NBR =W(i-1,j)+W(i,j-1) (4)

The sum of weights can be designed as W _SUM -(n-1)×wmax≈Wmax, where n is the number of pixels contained in the window. That is to say, when the similarity between all reference pixels in the window and the target pixel is very high (for example, the window is located in a flat area where the pixel value does not change much in the image), the weight of the target pixel (W _SUM -W _NBR =W _SUM -(n-1)×Wmax≈Wmax) is equivalent to the weight of any reference phase number. In one embodiment, W _SUM can be designed to be a power of 2, so that when implementing the division of Equation 2, it is only necessary to shift the binary value without using a divider.

When the target pixel is located on the edge of the image, the reference pixels also located on the edge in the window (that is, the reference pixels with high similarity to the target pixel) have a higher weight W, while other reference pixels not located on the edge ( That is, the weight W of the reference pixel with low similarity to the target pixel is lower, so the edge features of the filtered image are preserved, and the weight can be regarded as an edge preserve weight. In detail, the mechanism for generating the weight W by the weight calculation circuit 220 is related to the edge of the image. This mechanism enables the filtering processing of the image processing device 200 to have the ability to perceive the edge, so it can effectively suppress the image dragging phenomenon of IIR filtering to prevent color Distortion and blurred edges.

After obtaining the IIR filter value PV _IIR of the target pixel, the image processing device 200 outputs it to the subsequent circuit (not shown) (step S340 ), and stores it in the memory 210 for subsequent IIR filtering. program use.

FIG. 7 is a functional block diagram of another embodiment of the image processing apparatus of the present invention, and FIG. 8 is a flowchart corresponding to the image processing method of FIG. 7 . The image processing device 700 includes a memory 710 , weight calculation circuits 720 and 740 , and filter circuits 730 and 750 . The memory 710 stores reference pixels. When performing image processing, the image processing apparatus 700 first obtains a target pixel P(i, j), and uses a number of pixels adjacent to the target pixel P(i, j) as reference pixels (step S810 ). Next, the weight calculation circuit 720 generates a first weight W1 for each reference pixel based on the original pixel value PV _ORI of the target pixel and the reference pixel (step S820 ), that is, the first weight W1 is related to the similarity between the two pixels . Similarly, a functional block diagram of an example of the weight calculation circuit 720 is shown in FIG. 5 , and step S820 includes two sub-steps S322 and S324. In step S820, the pixel value PV _tar and the pixel value PV _ref input to the pixel similarity calculation circuit 510 are the original pixel value PV _ORI of the target pixel P(i, j) and the original pixel value PV _ORI of the reference pixel, respectively.

Next, the filter circuit 730 calculates the weighted average of the target pixel and the reference pixel according to the formula (3) to obtain the edge protection low-pass filter value PV _EPF (i,j) of the target pixel (step S830).

PV _EPP (i,j)=((W _SUM -W _NBR )×PV _ORI (i,j)+∑(w1×PV _ORI ))/W _SUM (5)

Equation (5) is the sum of a plurality of first products (W1×PV _ORI ) and second products ((W _SUM -W _NBR )×PV _ORI (i,j)) divided by the user-defined weight sum W _SUM . result. Wherein, W1 _NBR is the sum of the first weights W1 of all reference pixels, and the first product (W1×PV _ORI ) is the product of the first weight W1 of a reference pixel and the original pixel value of the reference pixel, ∑(W1× PV _ORI ) is the sum of the first products of all reference pixels. For example, taking the window of FIG. 9 as an example, the formula (5) becomes:

PV _EPF (i,j)=((W _SUM -W1 _NBR )×PV _ORI (i,j)+W1(i-1,j-1)×PV _ORI (i-1,j-1)+W1( i-1,j)×Pv _ORI (i-1,j)+W1(i-1,j+1)×PV _ORI (i-1,j+1)+W1(i,j-1)×PV _ORI (i,j-1)+W1(i,j+1)×PV _ORI (i,j+1)+W1(i+1,j-1)×PV _ORI (i+1,j-1) +W1(i+1,j)×PV _ORI (i+1,j)+W1(i+1,j+1)×PV _ORI (i+1,j+1))/W _SUM (6)

in,

W1 _NBR =W1(i-1,j-1)+W1(i-1,j)+W1(i-1,j+1)+W1(i,j-1)+W1(i,j+1 )+W1(i+1, j-1)+W1(i+1, j)+W1(i+1, j+1) (7)

In fact, steps S820-S830 are a finite impulse response (finiteimpulse response, FIR) filtering process with an edge protection mechanism. The edge protection low-pass filter value PV _EPF (i, j) of the target pixel is stored in the memory 710 for use in subsequent filtering procedures.

Next, in steps S840 and S850, the weight calculation circuit 740 and the filter circuit 750 further perform IIR filter processing on the edge protection low-pass filter value PV _EPF (i, j) to obtain the IIR filter value PV _IIR (i) of the target pixel , j). That is to say, steps S840 and S850 are substantially the same as steps S320 and S330, the difference is that the weight calculation circuit 740 and the filter circuit 750 use the edge protection low-pass filter value PV _EPF (i, j) of the target pixel instead of the original pixel value PV _OR I(I(i,j). Similarly, the functional block diagram of an embodiment of the weight calculation circuit 740 is shown in FIG. 5 . In step S840, the pixel value PV _tar of the pixel similarity calculation circuit 510 is input. and the pixel value PV _ref is respectively the edge protection low-pass filter value PV _EPF (i, j) of the target pixel P (i, j) and the IIR filter value PV _IIR of the reference pixel. Obtain a second weight of each reference pixel After W2 (similarly, the weight W2 is related to the similarity between the two pixels), the filter circuit 750 calculates the IIR filter value of the target pixel according to the formula (8).

PV _IIR (i, j)=((W _SUM -W2 _XBR )×PV _EPF (i, j)+∑(W2×PV _IIR )/W _SUM (8)

The formula (8) is similar to the formula (2) and will not be repeated here. Among them, W2 _NBR is the sum of the second weights W2 of all reference pixels. Taking FIG. 4 as an example,

W2 _NBR =W2(i-1,j)+W2(i,j-1) (9)

The embodiments of FIGS. 7 and 8 perform two filtering processes on the image, the first time is performed by the weight calculation circuit 720 and the filter circuit 730 (corresponding to steps S820 to S830 ), and the second time is performed by the weight calculation circuit 740 and the filter circuit 750 (corresponding to steps S840 to S850). The mechanism of the second filtering process is similar to that of the previous embodiment (corresponding to FIG. 2 and FIG. 3 ), and the purpose of the first filtering process is to further remove the high-frequency noise of the image and make the image clearer . The two filtering processes in the embodiments of FIG. 7 and FIG. 8 both implement an edge protection mechanism, that is, the first weight and the second weight are both edge protection weights. Therefore, the obtained filtering results can effectively suppress the image dragging phenomenon, so as to prevent the Prevents color distortion and blurred edges. The reference pixels of the first filtering process in the embodiments of FIGS. 7 and 8 include reference pixels of the second filtering process; for example, as shown in FIGS. 9 and 4 , the two reference pixels (P( i-1,j) and P(i,j-1)) are part of the eight reference pixels of FIG. 9 .

The present invention uses two threshold values to determine the allowable range of pixel similarity, so that a better trade-off can be achieved easily and effectively between the degree of image dragging phenomenon suppression and the degree of noise removal. Specifically, when the first and second thresholds are simultaneously lowered, the reference pixel must be closer to the target pixel in order to be given the highest weight Wmax, thus helping to suppress the image dragging phenomenon (that is, enhancing the edge protection of the image); When the first and second thresholds are simultaneously increased, the reference pixel has a higher probability of being given the highest weight Wmax, thus helping to remove noise (ie, make the image clearer). Increasing the difference between the first and second thresholds enables the filtering process to have better and more adaptive operational flexibility between suppressing the image dragging phenomenon and removing noise.

The weight calculation circuit and filter circuit of the present invention can be composed of adders, subtractors, multipliers, dividers, comparators, multiplexers, logic circuits and other elements. The weight calculation circuit 720 and the weight calculation circuit 740 in FIG. 7 may be the same set of hardware circuits or independent hardware circuits, and the filter circuit 730 and the filter circuit 750 may be the same set of hardware circuits or independent hardware circuits. The image processing device and the image processing method of the present invention are applicable to images of various color formats (such as RGB channels, YUV channels, grayscale images, etc.). Each channel of the color format is processed independently. Protect Weights) to help avoid color distortion.

Since a person with ordinary knowledge in the technical field can understand the implementation details and changes of the method invention in this application through the disclosure content of the device invention in this application, in order to avoid redundant repetition, the disclosure requirements and practicability of the method invention are not affected. On the premise, repeated descriptions are omitted here. Please note that the shapes, sizes, ratios, and steps of the components in the preceding figures are only schematic representations, which are for those skilled in the art to understand the present invention, and are not intended to limit the present invention.

Although the embodiments of the present invention are described above, these embodiments are not intended to limit the present invention. Those skilled in the art can change the technical features of the present invention according to the explicit or implicit contents of the present invention. All such changes may belong to the scope of patent protection sought by the present invention, in other words, the scope of patent protection of the present invention shall be determined by the scope of the patent application in this specification.

Claims (10)

1. An image processing method for filtering an image, comprising: (A) Generating a first weight for each first reference pixel, wherein the image includes a plurality of first reference pixels, and the size of the first weight corresponds to a target pixel and the first weight corresponding to the first weight The similarity between reference pixels is related; (B) calculating a first filter value of the target pixel according to the first weights, the pixel value of the target pixel and the pixel value of the first reference pixels; (C) generating a second weight for each second reference pixel, wherein the image includes a plurality of second reference pixels, and the size of the second weight corresponds to the target pixel and the second weight corresponding to the second weight The similarity between reference pixels is related, wherein the higher the similarity between the target pixel and the second reference pixel corresponding to the second weight, the greater the second weight; and (D) Calculate a second filter value of the target pixel according to the second weights, the first filter value of the target pixel, and the pixel values of the second reference pixels, wherein the pixels of the second reference pixels The value is the infinite impulse response filter value of the second reference pixels, and the second filter value of the target pixel is the infinite impulse response filter value of the target pixel. 2. The image processing method according to claim 1, wherein the step (B) comprises: calculating a plurality of first products, the first products are the products of the pixel values of the first reference pixels and the corresponding first weights; calculating a second product, the second product being the product of multiplying the pixel value of the target pixel by a difference between a predetermined weight and the sum of the first weights; and obtaining the first filter value of the target pixel according to the first products and the second product; and this step (D) includes: calculating a plurality of third products, the third products are the products of the pixel values of the second reference pixels and the corresponding second weights; calculating a fourth product, the fourth product being the product obtained by multiplying the first filter value of the target pixel by a difference between the predetermined weight and the sum of the second weights; and The second filter value of the target pixel is obtained according to the third products and the fourth product. 3 . The image processing method of claim 1 , wherein the pixel values of the first reference pixels are an original pixel value of the first reference pixels, and the pixel values of the second reference pixels are the first reference pixels. 4 . The second filtered value of the two reference pixels. 4. The image processing method according to claim 1 , wherein the step (A) and the step (B) are calculated based on an original pixel value of the first reference pixels and the target pixel, and the step (C) ) and the step (D) is to calculate the second filter value of the second reference pixels and the first filter value of the target pixel. 5. The image processing method according to claim 4, wherein the step (A) or the step (C) comprises: calculating an absolute difference between the first reference pixel or the second reference pixel and the target pixel; and The first weight or the second weight is determined according to the absolute difference, a first threshold, and a second threshold. 6. The image processing method of claim 1, wherein the second reference pixels are part of the first reference pixels. 7. An image processing device for filtering an image, comprising: a first weight calculation circuit for generating a first weight for each first reference pixel, wherein the image includes a plurality of first reference pixels, and the size of the first weight corresponds to a target pixel and the first weight The similarity between the first reference pixels is related; a first filter value calculation circuit, which calculates a first filter value of the target pixel according to the first weights, the pixel values of the target pixel and the pixel values of the first reference pixels; a second weight calculation circuit for generating a second weight for each second reference pixel, wherein the image includes a plurality of second reference pixels, and the size of the second weight corresponds to the target pixel and the second weight is related to the similarity between the second reference pixels, wherein the higher the similarity between the target pixel and the second reference pixel corresponding to the second weight, the greater the second weight; and a second filter value calculation circuit for calculating a second filter value of the target pixel according to the second weights, the first filter value of the target pixel and the pixel values of the second reference pixels, wherein the first filter values The pixel value of the two reference pixels is the infinite impulse response filter value of the second reference pixels, and the second filter value of the target pixel is the infinite impulse response filter value of the target pixel. 8. The image processing apparatus of claim 7, wherein the pixel values of the first reference pixels are an original pixel value of the first reference pixels, and the pixel values of the second reference pixels are the first reference pixels. The second filtered value of the two reference pixels. 9. An image processing method for filtering an image, comprising: (A) generating a weight for each reference pixel, wherein the image includes a plurality of reference pixels, and the size of the weight is related to the similarity between a target pixel and the reference pixel corresponding to the weight, wherein the The higher the similarity between the target pixel and the reference pixel corresponding to the weight, the higher the weight; and (B) performing an infinite impulse response filtering operation according to the weights, the pixel values of the target pixel and the pixel values of the reference pixels to obtain an infinite impulse response filtering value of the target pixel, wherein the The pixel values are infinite impulse response filtered values of these reference pixels. 10. The image processing method according to claim 9, wherein the step (B) comprises: calculating a plurality of first products, where the first products are the products of the pixel values of the reference pixels and the corresponding weights; calculating a second product, the second product being the product of multiplying the pixel value of the target pixel by a difference between a predetermined weight and the sum of the weights; and The infinite impulse response filter value of the target pixel is obtained according to the first products and the second products.

Images