Filtering and denoising system and filtering and denoising method for video data

Abstract

The invention relates to a filtering and denoising system and a filtering and denoising method for video data. An interframe data processing module, a frame data caching module and a frame data output control module are connected to form a data path, subtraction is carried out between the previous frame data and the current frame, a result worked out by the operation is multiplied by a filtering coefficient and then added by the current frame, so that the filtered current frame data are worked out, the frame data caching module comprises a DDR2 (Double Data Rate 2) memory, and has two caches, each cache caches the data of a frame of video image, so that the invention can be adapted to the denoising of the video images of an object moving at low speed, the time-weighted average of the image signals is worked out according to the frame period, so that the image signals are almost not affected, consequently, not only can the edges in the images be kept well, but also noise can be greatly reduced, and thereby the purpose of improving the signal-to-noise ratio of the images is achieved.

Description

technical field [0001] The present invention relates to the technical field of video surveillance, in particular, it is 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 video data interfered by random noise 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 rate improve...

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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