Video denoising method based on improved self-adaptive bilateral filter

Abstract

The invention discloses a video denoising method based on improved self-adaptive bilateral filter. The video denoising method includes that: 1) obtaining frames at two ends and the center of continuous N frames, and calculating a characteristic value of structure tensor of an image; 2) performing statistics on proportion of edge areas in the image and gradient distribution of the edges; 3) calculating gray average of pixels according to the direction of pixel structure tensor; 4) dividing an edge area and a non-edge area; 5) arranged gray variance in the edge area according to gradient size of the edges; 6) arranged large gray variance in the non-edge area; and 7) calculating the bilateral filter according to the self-adaptive gray average and the gray variance. The video denoising method based on the improved self-adaptive bilateral filter uses the front frames of a video as reference for edge statistics, reduces calculated amount, and enhances stability of the filter with the help of the characteristic that edge areas between adjacent frames are overlapped.

Description

technical field [0001] The invention relates to the technical field of computer video image processing, in particular to a video image denoising method based on improved self-adaptive bilateral filtering. Background technique [0002] With the use and promotion of multimedia information, the transmission of images, videos and other information on the network has become more and more popular, and has been widely valued by entertainment, social, business and other fields, bringing great convenience to various industries. Moreover, with the development of large-scale data visualization technology and its application in medical, aerospace and other fields, higher requirements are put forward for large-scale image and video storage and transmission technology. Due to the limitations of storage space and network transmission speed, images and videos need to be compressed to reduce the file size, and the compression process will generate some noise. In the process of transmission,...

AI Technical Summary

Problems solved by technology

However, many existing algorithms have considerable deficiencies in denoising effect and calculation load.

Median filtering and algorithms based on wavelet transform have a large amount of calculation, which cannot meet the real-time speed required by video processing. Gaussian filtering will cause serious blurring of image edge information, and bilateral filtering algorithms are powerless against salt and pepper noise.

And the above algorithm does not consider the relationship between adjacent frames in the video in terms of video denoising, which will cause flickering when the video is played

At present, there are many studies on video denoising algorithms based on inter-frame correlation, but they have not been widely used due to limited trial range and high algorithm complexity.

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