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24 August 2017 A low-light-level video recursive filtering technology based on the three-dimensional coefficients
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Abstract
Low light level video is an important method of observation under low illumination condition, but the SNR of low light level video is low, the effect of observation is poor, so the noise reduction processing must be carried out. Low light level video noise mainly includes Gauss noise, Poisson noise, impulse noise, fixed pattern noise and dark current noise. In order to remove the noise in low-light-level video effectively, improve the quality of low-light-level video. This paper presents an improved time domain recursive filtering algorithm with three dimensional filtering coefficients. This algorithm makes use of the correlation between the temporal domain of the video sequence. In the video sequences, the proposed algorithm adaptively adjusts the local window filtering coefficients in space and time by motion estimation techniques, for the different pixel points of the same frame of the image, the different weighted coefficients are used. It can reduce the image tail, and ensure the noise reduction effect well. Before the noise reduction, a pretreatment based on boxfilter is used to reduce the complexity of the algorithm and improve the speed of the it. In order to enhance the visual effect of low-light-level video, an image enhancement algorithm based on guided image filter is used to enhance the edge of the video details. The results of experiment show that the hybrid algorithm can remove the noise of the low-light-level video effectively, enhance the edge feature and heighten the visual effects of video.
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© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rongguo Fu, Shu Feng, Tianyu Shen, Hao Luo, Yifang Wei, and Qi Yang "A low-light-level video recursive filtering technology based on the three-dimensional coefficients", Proc. SPIE 10395, Optics and Photonics for Information Processing XI, 103950V (24 August 2017); https://doi.org/10.1117/12.2276978
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