Robust Kronecker product video denoising based on fractional-order total variation model

Abstract

Most existing sparse representation-based video denoising algorithms assume video noise is additive Gaussian white noise, which is often violated in practice. In this paper, a robust Kronecker product video denoising (RKPVD) algorithm based on fractional-order total variation model is proposed to remove serious mixed Gaussian-impulse noises from the video data. Using the temporal and spatial correlations of videos, the problem of denoising mixed noises is formulated as a robust low rank video recovery minimization problem based on fractional-order total variation (FTV) model. The resulting under-determined minimization problem, which consists of nuclear norm, Kronecker product sparse ℓ1 norm and FTV, can be efficiently solved by a two-stage algorithm combined with alternating direction method (ADM). The robustness and effectiveness of the proposed RKPVD denoising algorithm on removing mixed Gaussian impulsive noise are validated in the experiments. Compared with several state-of-the-art algorithms, such as total variation (TV), sparse and redundant representation (SARR), video block matching and 3D filtering (VBM3D), robust principal component analysis (RPCA) and robust temporal spatial decomposition (RTSD), intensive experiments show that the proposed RKPVD method has a higher PSNR (peak signal-to-noise ratio) and a better visual detail preservation.