Method for denoising and filtering flat area

Abstract

The invention provides a method for denoising and filtering a flat area, which comprises the following steps of: S1, determining a block to be filtered which meets the following two conditions: 1) the number of gray values is 2, and the difference between the two gray values is 1; 2) the number of one gray value is larger than a set threshold value, and the number of the other gray value is smaller than the set threshold value, and the gray values are called a large probability gray value b and a small probability gray value s respectively; S2, denoising and filtering: 2.1, when the pixel point spacing is set to be 1, calculating the gray level co-occurrence matrix of the to-be-filtered block, judging the distribution condition of small-probability gray levels, wherein the smaller the number of the adjacent small-probability gray levels is, the larger the sum of diagonal lines is, and the more discrete the small-probability points are; on the contrary, the textures of the small probability points are thicker; reducing the filtering intensity of the small-probability gray block of the coarse texture; increasing the filtering intensity of discrete small-probability gray blocks; and S2.2, reducing blurring of the coarse texture or eliminating of the coarse texture. According to the method, the strong noise in the flat area is filtered, the residual error information amount is reduced, and the noise consumption of the code stream is further reduced.

Description

technical field [0001] The invention relates to the field of image technology, in particular to a method for denoising and filtering flat areas. Background technique [0002] Image filtering is to suppress the noise of the target image under the condition of preserving the image details as much as possible. Most of the filtering algorithms are smoothing filtering, that is, spatial filtering technology with low frequency enhancement. The smoothing filter algorithm generally uses a window containing weighted coefficients as a filter, walks through each pixel of the image, and obtains the filtered pixel value through a weighted calculation method. [0003] However, the defect in the prior art is that the small probability points in the block to be filtered are uneven factors, which is not conducive to the concentration of energy after prediction transformation. In addition, the smoothing filter algorithm has problems such as large amount of calculation, many changes to image ...

AI Technical Summary

Problems solved by technology

[0003] However, the defect in the existing technology is that the small probability points in the block to be filtered are uneven factors, which is not conducive to the concentration of energy after prediction transformation

In addition, the smoothing filter algorithm has problems such as large amount of calculation, many changes to image pixel values, blurred image detail edges, etc., and is not suitable for real-time encoding and subjective viewing of images.

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