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Patch-Based Methods for Video Denoising

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Denoising of Photographic Images and Video

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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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.

The authors were supported by grant TIN2014-53772R and TIN2017-85572-P.

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Notes

  1. 1.

    We used the Matlab implementations of Ji et al. (by Sibin, Yuhong, and Yu, 2013), and VIDOLSAT (from http://www.ifp.illinois.edu/~yoram). The rest of algorithms were implemented following the descriptions in the published papers. Remark that Gao et al. method is an extension to video sequences of the method described in [65].

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  1. DMI - IAC3, Universitat Illes Balears, Ctra Valldemossa km 7.5, Palma, Spain

    A. Buades & J. L. Lisani

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  1. Pompeu Fabra University, Barcelona, Spain

    Marcelo Bertalmío

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Buades, A., Lisani, J.L. (2018). Patch-Based Methods for Video Denoising. In: Bertalmío, M. (eds) Denoising of Photographic Images and Video. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-96029-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-96029-6_7

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