Video Denoising and Enhancement via Dynamic Video Layering

TL;DR

This paper introduces a novel video denoising method that decomposes videos into low-rank, sparse, and residual layers, outperforming existing algorithms through extensive experiments.

Contribution

The work presents a new layered decomposition approach for video denoising, leveraging low-rank and sparse modeling to improve noise removal effectiveness.

Findings

Outperforms state-of-the-art denoising algorithms

Effective separation of noise into sparse and residual layers

Robust performance across various noisy video datasets

Abstract

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 many noisy or corrupted videos can be split into three parts - the "low-rank layer", the "sparse layer", and a small residual (which is small and bounded). We show, using extensive experiments, that our denoising approach outperforms the state-of-the-art denoising algorithms.