Robust video denoising using low rank matrix completion

Abstract

Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recent developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against a few state-of-art denoising algorithms.

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

“…[15], and ATA [2]. VBM3D and ATA methods only consider Gaussian white noise and do not work well on salt & pepper noise, as shown in Figs.…”

“…The idea of sparse representation using a patch dictionary has also been applied to video denoising [5,14], where the denoised image patches are found by seeking the sparsest solution in a patch dictionary. The idea of low rank has also been used to remove noise from video data [15]. In Ref.…”

“…But surveillance video sequences usually have very high temporal redundancy which means neighboring frames have high similarity in structure which should be effectively used for better performance [2,7,15]. Patch-based schemes can also be applied to video sequences, but unlike in single image denoising, the block matching process is not only applied within each image to which the referenced patch belongs, but also within neighboring frames.…”

“…In Ref. [15], similar small patches are grouped and reformed as a matrix, then implausible items are removed and rules are used to preserve only the most reliable data. Finally the video denoising problem is formulated as low-rank matrix completion with many elements missing.…”

Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

“…Since matrix rank is not a convex function, its convex surrogate (i.e., the matrix nuclear norm) is used for approximation and efficiently solved (Cai et al 2010;Ma et al 2011;Recht et al 2010;Peng et al 2011) with numerous applications including face recognition (Peng et al 2011), image retrieval , subspace clustering , image classification (Zhang et al 2013b), background subtraction (Candès et al 2011), and video denoising (Ji et al 2010), among others.…”