An Adaptive 3D Noise Reduction Method for Low SNR Video in Fixed Scene

Abstract

The invention discloses a self-adaptive 3D (Three-Dimensional) noise reduction method for a low signal-to-noise ratio video under a fixed scene. The method organically combines the 'estimating and updating of background and 'time domain filter', thus the real-time background is estimated, and the noise is greatly inhibited, and as a result, a very clear background area is gained. When detecting a moving object, a block decision method is carried out to determine a motion pixel block, which brings little calculated amount, and redundant parameter settings are not needed, so that the effect of compensating the motion detection is realized, and the regular quadrate blocks are beneficial to the follow-up partition processing. When the background is updated, an average frame is updated into a background frame with the relatively large weight coefficient, thus the real-time variation of the light can be adapted well, the capacity of resisting the disturbance is improved, and a 'block flickering' effect is avoided; only a macro block corresponding to the position of a background area is updated, so that the background can be kept clean; and a binary threshold can be estimated along with the real-time variation of the video sequence noise variance, therefore, the accuracy of estimation is ensured, and high self-adaptation is gained.

Description

technical field [0001] The invention relates to the field of video noise reduction research, in particular to an adaptive 3D noise reduction method based on background difference and block judgment based on time-space domain joint. Background technique [0002] Previous video image noise reduction methods mainly include spatial filtering, temporal filtering and joint temporal-spatial filtering. Although spatial filtering can filter out some noise, it does not use the relationship between video frames, which can easily cause loss of image details. Temporal filtering utilizes the inter-frame correlation of video and is very suitable for video noise reduction, but it is more suitable for still video processing, otherwise serious "smearing" blur will occur. [0003] Joint filtering in spatio-temporal domain (namely 3D filtering) is currently a research hotspot in video noise reduction. Some scholars adopt the method of time domain filtering first and then spatial domain filter...

AI Technical Summary

Problems solved by technology

Some scholars adopt the method of time domain filtering first and then spatial domain filtering. Although the noise reduction effect is more obvious than that of time domain filtering alone, it loses certain details and does not solve the defect of time domain filtering, that is, moving objects will produce " "smear" blur; some scholars have proposed an intelligent video sequence noise reduction algorithm based on motion detection, which judges whether there is motion by comparing the gray average value of the corresponding blocks of the current frame and the reference frame. When more than 80% of the blocks meet the conditions, it is judged as a static area, and time domain filtering is performed; otherwise, spatial domain filtering is performed, but this will cause more static pixels to be misjudged as moving pixels, reducing the denoising effect; The video noise reduction method based on temporal filtering of background extraction and adaptive filtering in pixel domain uses background difference method to extract moving areas, and adaptively uses different spatial filtering methods for moving areas, but this method has the following disadvantages: a , With the passage of time, new objects will appear in the background or the original objects will disappear, and the background will continue to change with the external light, but the background participating in the difference will remain unchanged, which will lead to the inability to effectively extract after a period of time The moving area causes misjudgment of moving targets, and the processed video is seriously distorted; b. Poor anti-interference and sensitive to light. If the local light intensity of the image changes, it is easy to produce serious "block flicker" effect

Some scholars have proposed a 3D noise reduction algorithm based on motion compensation, which finds forward and backward matching blocks through motion estimation, uses temporal filtering for macroblocks with low motion intensity, and uses spatial bilateral filtering for macroblocks with high motion intensity. When the lens is not moving, it takes a lot of time to estimate the motion of each macroblock, the processing speed is slow, and it is not suitable for real-time processing of video in the case of fixed lens

[0004] To sum up, the existing spatio-temporal joint video noise reduction methods often have "smearing" blur, poor noise reduction effect, sensitivity to light, easy to produce "block flicker" effect, large amount of calculation, and are not suitable for real-time video systems and other shortcomings

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