Non-local mean space domain time varying video filtering method

Abstract

The invention discloses a non-local mean space domain time varying video filtering method, mainly solving the problem that the existing non-local mean space domain time varying filter is large in computation amount and restricted in application range. The filtering process includes that: weight normalization coefficient and value after non-normalization filtering at all the points of the current frame image are initialized to be zero; for polarization of each coordinate in search domain, all the pixel points of the current frame image are subject to uniform pre-treatment, then a weighting verifying value of all the pixel points of the current frame image at the coordinate in the search domain is rapidly calculated; the weight normalization coefficient and the value after non-normalization filtering are updated according to the weighting verifying value; and the image after filtering is obtained by calculation according to the weight normalization coefficient and the value after non-normalization filtering. The invention greatly reduces the computational complexity of the existing non-local mean space domain time varying filtering method and can be applicable to the video de-noising field.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a video filtering method, in particular to a time-varying video filtering method in non-local mean value space, which is used for processing digital video. technical background [0002] With the development of information and multimedia technology, the application of digital video is more and more extensive. Spatial filtering technology is one of the basic methods of digital image processing, and has a wide range of applications in the field of digital video, such as image denoising for images in video streams. The basic idea of ​​spatial filtering is to use the relationship between adjacent pixels to obtain the corresponding processing purpose. The shape composed of adjacent pixels is called the search domain. For any pixel in the image, the filtered value of this point is equal to the normalized weighted and summed value of all points in the search domain centered on this point....

AI Technical Summary

Problems solved by technology

[0020] The above-mentioned non-local mean filter video filtering method, when calculating the image offset function of the combination of adjacent pixels and search domain offsets, the phenomenon of image matching window coverage will occur, which will lead to the covered matching window Computational redundancy problem of performing multiple comparison calculations between pixels and search field offset pixel combinations

In the point-by-point filtering process of the above-mentioned non-local mean filtering method, the weighted kernel weights of each search domain bias point of each pixel in the current frame image need to be recalculated, which will cause a large number of repeated calculations

The extremely high computational complexity of the existing non-local mean filter video filtering method results in too long calculation time, which greatly limits the application of non-local mean filter in the field of video processing, such as in real-time embedded systems It is not possible to apply this computationally complex non-local mean filter

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