Video stream noise reduction method and device, and storage medium

Abstract

The invention provides a video stream noise reduction method and device, and a storage medium. The method comprises steps of acquiring first image texture in a video stream and second image texture after noise reduction; according to a current real-time filter parameter of a filter system, performing linear interpolation calculation on the first image texture and the second image texture; and outputting first image texture after noise reduction, wherein the first image is a current to-be-processed image in the video stream, and the second image is a previous frame of image of the first image.Through adoption of the method, real-time noise reduction of the video stream image is realized, noise reduction effect is obvious, and the problem that afterimage exists in the video stream is solved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular, to a video stream noise reduction method, device, and storage medium. Background technique [0002] Image and video denoising is to remove the noise introduced in the process of image and video acquisition and transmission, so as to improve image quality and facilitate subsequent processing. Therefore, image and video denoising is a very important link in the image processing process. [0003] In terms of video denoising, the existing technical solutions use an adaptive Kalman filter video denoising system. Video denoising itself is a video filtering process. The system uses a Kalman filter with a fixed variance parameter, but if the measurement variance is relative to If the prediction variance is small, the video noise reduction effect of the system is not obvious, and the image after filtering will have afterimages. In addition, existing video...

AI Technical Summary

Problems solved by technology

[0003] In terms of video denoising, the existing technical solutions use an adaptive Kalman filter video denoising system. Video denoising itself is a video filtering process. The system uses a Kalman filter with a fixed variance parameter, but if the measurement variance is relative to If the prediction variance is small, the video noise reduction effect of the system is not obvious, and the image after filtering will have afterimages

In addition, the existing video stream noise reduction solutions require high computational consumption, which cannot meet the real-time noise reduction processing of the mobile terminal.

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