CN113628118B - Method for denoising and filtering in flat area - Google Patents
Method for denoising and filtering in flat area Download PDFInfo
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- CN113628118B CN113628118B CN202010371888.5A CN202010371888A CN113628118B CN 113628118 B CN113628118 B CN 113628118B CN 202010371888 A CN202010371888 A CN 202010371888A CN 113628118 B CN113628118 B CN 113628118B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
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Abstract
The application provides a flat area denoising filtering method, which comprises the following steps: s1, determining a block to be filtered, wherein the following two conditions are required to be met: 1) The number of the gray values is 2, and the difference between the two gray values is 1; 2) One gray value number is larger than a set threshold value, and the other gray value number is smaller than the set threshold value, which are respectively called a large probability gray b and a small probability gray s; s2, denoising and filtering: s2.1, when the pixel point distance is set to be 1, calculating a gray level co-occurrence matrix of the block to be filtered, judging the distribution condition of small probability gray levels, wherein the smaller the number of adjacent small probability gray levels is, the larger the sum of diagonal lines is, and the more discrete the small probability points are; otherwise, the thicker the texture of the small probability point is; reducing the filtering strength for the small probability gray scale blocks of the coarse texture; increasing the filtering intensity for the discrete small probability gray scale blocks; s2.2, blurring or elimination of coarse textures is reduced. The method filters strong noise in a flat area, reduces the information quantity of prediction residual errors, and further reduces the consumption of code streams on noise.
Description
Technical Field
The application relates to the technical field of images, in particular to a flat area denoising filtering method.
Background
And image filtering, namely suppressing noise of the target image under the condition of retaining the detail characteristics of the image as much as possible. Most filtering algorithms are smooth filtering, i.e. low frequency enhanced spatial filtering techniques. The smoothing filtering algorithm generally adopts a window containing weighting coefficients as a filter, and the window is moved to each pixel point of the image, and the filtered pixel value is obtained through a weighting calculation method.
However, the defects in the prior art are that: the small probability points in the block to be filtered are uneven factors, which are unfavorable for predicting the concentration of energy after transformation. In addition, the smoothing filter algorithm has the problems of large calculated amount, more change of image pixel values, blurred image detail edges and the like, and is not suitable for real-time encoding and subjective viewing of images.
Furthermore, the common terminology in the prior art is as follows:
gray level co-occurrence matrix: a common method for describing textures by researching gray space correlation characteristics is that the textures are formed by repeatedly appearing gray distribution at space positions, so that a certain gray relation exists between two pixels which are separated by a certain distance in an image space. If the image is made up of blocks of pixels with similar gray values, the diagonal elements of the gray co-occurrence matrix will have relatively large values; if the gray values of the pixels of the image vary locally, then the elements that deviate from the diagonal will have a relatively large value.
Disclosure of Invention
In order to solve the above problems, an object of the present application is to: the small probability points in the block to be filtered are uneven factors, which are not beneficial to predicting the concentration of energy after transformation, and the large probability gray scale is required to be close by a filtering algorithm. Meanwhile, the true brightness layering sense of the original pixel value corresponding to the large-probability gray level is protected.
Specifically, the application provides a flat region denoising filtering method, which comprises the following steps:
s1, determining a block to be filtered, wherein the following two conditions are required to be met:
1) The number of the gray values is 2, and the difference between the two gray values is 1;
2) One gray value number is larger than a set threshold value, and the other gray value number is smaller than the set threshold value, which are respectively called a large probability gray b and a small probability gray s;
s2, denoising and filtering:
s2.1, when the pixel point distance is set to be 1, calculating a gray level co-occurrence matrix of the block to be filtered, judging the distribution condition of small probability gray levels, wherein the smaller the number of adjacent small probability gray levels is, the larger the sum of diagonal lines is, and the more discrete the small probability points are; otherwise, the thicker the texture of the small probability point is; reducing the filtering strength for the small probability gray scale blocks of the coarse texture; increasing the filtering intensity for the discrete small probability gray scale blocks;
s2.2, blurring or elimination of coarse textures is reduced.
The threshold in S1 is set to 220.
The small probability points in the block to be filtered in the S1 are uneven factors, which are unfavorable for the concentration of the energy after the prediction transformation, and the large probability gray scale is required to be close by a filtering algorithm.
The gray level co-occurrence matrix in the S2.1 is
The processing of the S2 denoising filter further comprises:
(1) Traversing each small-probability gray scale point to be filtered by taking 3x3 as a filtering window, wherein when the number of the large-probability gray scale points around the filtering point is larger, the filtering weight is larger, and the filtered value is closer to the large-probability gray scale;
(2) Denoising and filtering calculation:
1) When the filter block contains coarser texture, the filter strength is reduced
2) When the filter block is a noise discrete block, the filter strength increases
Wherein the pixel old And pixel new Respectively the pixel values before and after filtering, cnt max Pixels are the number of the large probability gray points in the window min And pixel avg Is the minimum pixel value and average value in the large probability gray scale in the window.
Thus, the present application has the advantages that: through analysis, only stronger noise in the flat area can be better identified, so that the method filters the stronger noise in the flat area, reduces the information quantity of prediction residual errors, is more beneficial to the concentration of residual error energy and the reduction of high-frequency components in the transformation process, and further reduces the consumption of code streams on noise.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate and together with the description serve to explain the application.
FIG. 1 is a schematic flow chart of the method of the present application.
Detailed Description
In order that the technical content and advantages of the present application may be more clearly understood, a further detailed description of the present application will now be made with reference to the accompanying drawings.
As shown in fig. 1, the present application relates to a method for denoising and filtering a flat region, which comprises the following steps:
s1, determining a block to be filtered, wherein the following two conditions are required to be met:
1) The number of the gray values is 2, and the difference between the two gray values is 1;
2) One gray value number is larger than a set threshold value, and the other gray value number is smaller than the set threshold value, which are respectively called a large probability gray b and a small probability gray s;
s2, denoising and filtering:
s2.1, when the pixel point distance is set to be 1, calculating a gray level co-occurrence matrix of the block to be filtered, judging the distribution condition of small probability gray levels, wherein the smaller the number of adjacent small probability gray levels is, the larger the sum of diagonal lines is, and the more discrete the small probability points are; otherwise, the thicker the texture of the small probability point is; reducing the filtering strength for the small probability gray scale blocks of the coarse texture; increasing the filtering intensity for the discrete small probability gray scale blocks;
s2.2, blurring or elimination of coarse textures is reduced.
In particular, since the stronger noise of the flat area is easier to identify and remove, in order to reduce the change of the pixel value of the image less, the application only filters the stronger noise of the flat area so as to preserve the brightness layering sense formed by rich gray levels.
1. Determining a block to be filtered
The blocks satisfying the following two conditions are blocks to be filtered:
1) The number of the gray values is 2, and the difference between the two gray values is 1
2) One number of gray values is greater than the set threshold while the other number of gray values is less than the set threshold. Respectively referred to as a large probability gray scale b and a small probability gray scale s. The proposal threshold is set to 220.
The general small probability gray scale is in the following distribution:
| 40 | 40 | 40 | 40 | 40 | 39 | 39 | 39 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 39 | 39 | 39 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 39 | 39 | 39 | 39 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 39 | 39 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 39 | 39 | 39 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 39 | 39 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 39 | 39 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 39 | 39 | 39 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 39 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 9 | 40 | 40 | 40 | 40 | 40 | 39 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 9 | 40 | 40 | 40 | 40 | 40 | 40 | 39 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 39 |
| 40 | 40 | 39 | 39 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 39 | 39 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 9 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
| 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 | 40 |
the small probability points in the block to be filtered are uneven factors, which are not beneficial to predicting the concentration of energy after transformation, and the compression rate is improved on the premise of not influencing the image content by approaching the large probability gray scale through a filtering algorithm. Meanwhile, the true brightness layering sense of the original pixel value corresponding to the large-probability gray level is protected.
2. Denoising filter
When the pixel point distance is set to be 1, calculating a gray level co-occurrence matrix of the block to be filtered, judging the distribution condition of the small probability gray level, wherein the smaller the number of adjacent small probability gray levels is, the larger the sum of diagonal lines is, and the more discrete the small probability points are; whereas the coarser the texture of the low probability points. Reducing the filtering strength for the small probability gray scale blocks of coarser textures; the filter strength is increased for discrete small probability gray blocks. Reducing blurring or elimination of coarser textures. Let the threshold gray_thr=220.
Gray scale co-occurrence matrix
And traversing each small-probability gray scale point to be filtered by taking 3x3 as a filtering window, wherein when the number of the large-probability gray scale points around the filtering point is larger, the filtering weight is larger, and the filtered value is closer to the large-probability gray scale. The specific denoising processing method comprises the following steps:
1) When the filter block contains coarser texture, the filter strength is reduced
2) When the filter block is a noise discrete block, the filter strength increases
Wherein the pixel old And pixel new Respectively the pixel values before and after filtering, cnt max Is the big probability ash in the windowThe number of points, pixel min And pixel avg Is the minimum pixel value and average value in the large probability gray scale in the window.
After the image denoised by the method is subjected to predictive transformation, residual energy is more easily concentrated to low-frequency components, and the coefficient value of high-frequency residues is reduced or quantized to 0 in the quantization process, so that the consumption of noise in a code stream is reduced. The smaller the quantization parameter, the more the code stream is reduced. The application has small calculated amount, and is more beneficial to image compression on the premise of not blurring image details and retaining brightness layering sense of a flat area.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, and various modifications and variations can be made to the embodiments of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (7)
1. A method of flat region denoising filtering, comprising the steps of:
s1, determining a block to be filtered, wherein the following two conditions are required to be met:
1) The number of the gray values is 2, and the difference between the two gray values is 1;
2) One gray value number is larger than a set threshold value, and the other gray value number is smaller than the set threshold value, which are respectively called a large probability gray b and a small probability gray s;
s2, denoising and filtering:
s2.1, when the pixel point distance is set to be 1, calculating a gray level co-occurrence matrix of the block to be filtered, judging the distribution condition of small probability gray levels, wherein the smaller the number of adjacent small probability gray levels is, the larger the sum of diagonal lines is, and the more discrete the small probability points are; otherwise, the thicker the texture of the small probability point is; reducing the filtering strength for the small probability gray scale blocks of the coarse texture; increasing the filtering intensity for the discrete small probability gray scale blocks;
s2.2, blurring or elimination of coarse textures is reduced.
2. The method of flat region denoising filtering according to claim 1, wherein the threshold in S1 is set to 220.
3. The method for denoising and filtering a flat area according to claim 1, wherein the small probability points in the block to be filtered in S1 are uneven factors, which are unfavorable for the concentration of the energy after the prediction transformation, and require the approach to the large probability gray scale by a filtering algorithm.
4. The method of flat region denoising and filtering according to claim 1, wherein the gray level co-occurrence matrix in S2.1 is
5. The method of flat region denoising filtering according to claim 1, wherein the processing of S2 denoising filtering further comprises:
(1) Traversing each small-probability gray scale point to be filtered by taking 3x3 as a filtering window, wherein when the number of the large-probability gray scale points around the filtering point is larger, the filtering weight is larger, and the filtered value is closer to the large-probability gray scale;
(2) Denoising and filtering calculation:
1) When the filter block contains coarser texture, the filter strength is reduced
2) When the filter block is a noise discrete block, the filter strength increases
Wherein the pixel old And pixel new Respectively the pixel values before and after filtering, cnt max Pixels are the number of the large probability gray points in the window min And pixel avg Is the minimum pixel value and average value in the large probability gray scale in the window.
6. The method of flat region denoising filtering according to claim 5, wherein 1) when the filter block contains coarser texture, the filtering strength is reduced:
7. the method of flat region denoising filtering according to claim 5, wherein 2) when the filtering block is a noise discrete block, the filtering strength increases:
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