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• W2890435611 abstract "Video denoising is an important and open problem, which is less treated than the single-image case. Most image sequence denoising techniques rely on still image denoising algorithms; however, it is possible to take advantage of the redundant information contained in the sequence to improve the denoising results. Most recent algorithms are patch based. These methods have two clearly differentiated steps: select similar patches to a reference one and estimate a noise-free version from this group. We review selection and estimation strategies. In particular, we show that the performance is improved by introducing motion compensation. We use as example a recent video denoising technique inspired by fusion algorithms that use motion compensation by regularized optical flow methods, which permits robust patch comparison in a spatiotemporal volume. The use of principal component analysis ensures the correct preservation of fine texture and details, provided that the noise is Gaussian and white, with known variance. Video acquired by any video camera or mobile phone undergoes several processings from the sensor to the final output. This processing, including at least demosaicking, white balance, gamma correction, filtering, and compression, makes a white noise model unrealistic. Indeed, real video captured in dark environments has a very poor quality, with severe spatially and temporally correlated noise. We discuss a denoising framework including realistic noise estimation, multiscale processing, variance stabilization, and white noise removal algorithms. We illustrate the performance of such a chain with real dark and compressed movie sequences." @default.
• W2890435611 created "2018-09-27" @default.
• W2890435611 creator A5010434823 @default.
• W2890435611 creator A5028410237 @default.
• W2890435611 date "2018-01-01" @default.
• W2890435611 modified "2023-09-27" @default.
• W2890435611 title "Patch-Based Methods for Video Denoising" @default.
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• W2890435611 doi "https://doi.org/10.1007/978-3-319-96029-6_7" @default.
• W2890435611 hasPublicationYear "2018" @default.
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