Frame data filtering and denoising system and method

Abstract

The invention relates to a video data filtering and denoising system and method; the video data filtering and denoising system is characterized in that an interframe data processing module, a frame data buffer module and a frame data output control module are connected into a data path; the data of two previous frames and the data of a current frame respectively are multiplied by filtering coefficients for obtaining sums; the obtained sums are averaged to obtain the data of the current filtered frame; the frame data buffer module is formed by a DDR2 (Double Data Rate 2) memory which is provided with three cache regions, wherein each cache region respectively caches the data of a video image of one frame, thereby being capable of adapting to the denoising of a video image of a low-speed moving object; and by carrying out temporal weighted average on the image signal according to a frame period, the edge of the image can be very well reserved with hardly any influence on an image signal, and the noise can greatly lowered, thereby achieving the purpose of improving the signal-to-noise ratio of an image.

Description

technical field [0001] The present invention relates to the technical field of video monitoring, specifically a frame data processing module composed of an inter-frame data processing module, a frame data cache module and a frame data output control module, which is applied to noise reduction of real-time video and effectively filters out random noise interference A filter noise reduction system and a filter noise reduction method. Background technique [0002] The main goal of video image denoising is to filter out the random noise, while keeping the detail information as good as possible, and to reduce the visual degradation caused by filtering in the denoised video image. [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, and frame rat...

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 various noise levels. Adaptation mechanism can improve the defect of pixel domain noise reduction algorithm

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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