Filtering noise reduction system and method for video data

Abstract

The invention discloses a filtering noise reduction system and a filtering noise reduction method for video data. The method comprises the following steps: combining a mean filtering algorithm and a time-domain recursive filtering algorithm; generating a 5*5 neighborhood template through a neighborhood template generation module; dividing the sum of video image data of five columns of the 5*5 neighborhood template by a constant factor, so as to obtain a frame output; replacing data of an original frame by the mean of data of adjacent 25 frames in space so as to effectively filtrate noise caused by data stack in frames; allowing video image data subjected to mean filtering processing to be subjected to time-domain recursive filtering; carrying out subtraction operation between two adjacent frames; and multiplying the calculated result by a filtering coefficient, and then adding the second result with a current frame data so as to obtain the data of the current frame subjected to filtering. Therefore, the method is suitable denoising for the video images of low-velocity moving objects; and the frame duration of image signal is subjected to time weighted average, and the image signal is barely affected, so that not only are the edges of the images well reserved, but also the noise between frames caused by data stack can be eliminated.

Description

technical field [0001] The invention relates to the technical field of video monitoring, in particular to a filter noise reduction system and a filter noise reduction method that are applied to real-time video noise reduction and can simultaneously effectively filter out the noise superimposed on intra-frame data and inter-frame data. Background technique [0002] The noise mixed in the process of image signal acquisition by the image sensor is mainly Gaussian noise, and the main goal of video image noise reduction is to filter out the noise, while keeping the details as good as possible, and to reduce noise in the video image after noise reduction. Visual degradation due to filtering. [0003] Video image noise reduction technology can not only filter out the noise in the video image and improve the subjective visual quality of the video image, but also has important significance for subsequent processing tasks such as compression coding, target recognition and tracking, an...

AI Technical Summary

Problems solved by technology

The main disadvantage of the pixel domain noise reduction algorithm is that it is easy to introduce spatial domain degradation such as time domain degradation and over-smoothing into the denoised video image. So far, there is no denoising algorithm suitable for multiple noise levels.

In addition, in the pixel domain noise reduction algorithm, motion estimation is often used to obtain relevant information in the time domain, but the existence of noise easily affects the accuracy of motion estimation, thereby reducing the performance of noise reduction.

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