Video denoising via online sparse and low-rank matrix decomposition

Video denoising refers to the problem of removing “noise” from a video sequence. Here the term “noise” is used in a broad sense to refer to any corruption or outlier or interference that is not the quantity of interest. In this work, we develop a novel approach to video denoising that is based on the idea that most noisy or corrupted videos can be split into two parts - the approximate “low-rank” layer and the “sparse layer”. We first splitting the given video into these two layers, and then apply an existing state-of-the-art denoising algorithm on each layer. We show, using extensive experiments, that our denoising approach outperforms the state-of-the art denoising algorithms.