In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial\nredundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully\nin various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted\nsum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch\nintensity vector distances. The process requires computing vector distances for all of the patches in the search window.\nDirect extension of this method from 2D to 3D, for video processing, can be computationally demanding. Note that\nthe size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to\nform the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing.\nHere, we propose a novel recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage\nof recursion for computational savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is\nstill able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our\napproach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum\nof pixels from the current frame and a pixel from the previous frame estimate. Only the single best matching patch\nfrom the previous estimate is incorporated into the current estimate. Several experimental results are presented here\nto demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality.
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