Motion-adaptive video image denoising method and device

Abstract

The invention relates to the field of video image processing and discloses motion-adaptive video image denoising method and device. Time-domain denoising strength is controlled by determining motion level of a current frame to be processed, and noise level of the frame, to be processed, subjected to time-domain filtering is updated. The frame to be processed is subjected to space-domain filtering according to the updated noise level. Therefore, strength in time-domain denoising and space-domain denoising is adaptively adjusted. When the motion level decreases, the time-domain denoising strengthens and the space-domain denoising weakens; on the contrary, the time-domain denoising strength decreases and the space-time denoising strength increases; therefore, better denoising is achieved, the problem that video-denoised images blur and motion objects are 'tailed' are well solved, and system implementation cost can be effectively controlled.

Description

Technical field [0001] The invention relates to the field of video image processing, and in particular to video image noise reduction technology. Background technique [0002] In the field of video image processing, noise reduction is a very effective method to enhance video image quality. Noise will inevitably be introduced in the process of video collection, transmission and reception. In order to improve the subjective and objective effects of video images, improve the compression efficiency of video images or save transmission bandwidth, it is necessary to perform noise reduction processing on videos. Video noise is usually modeled as zero-mean Gaussian white noise, with noise between frames independent of each other and noise within frames occurring randomly. Video noise reduction is usually processed in the spatial domain, time domain, transform domain, or a combination of different domains to achieve better visual effects. Spatial noise reduction uses the correlatio...

AI Technical Summary

Problems solved by technology

Spatial noise reduction utilizes the characteristic that the pixels in the image neighborhood have correlation but the noise has no correlation to eliminate noise; the spatial noise reduction mostly adopts the method of low-pass filtering, which can effectively filter out the high-frequency noise of the image, but at the same time it will also blur the edge of the image or detail, the stronger the denoising, the blurrier the image

Temporal denoising utilizes the feature that images between video frames are correlated but noise is random and irrelevant to eliminate noise; temporal denoising mostly uses multi-frame weighting or recursive weighting methods; temporal denoising can effectively remove Noise protects image boundaries and details at the same time; but simple inter-frame weighting can cause "smearing" of moving objects, making moving images blurred

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