An implementation of 'Efficient Multi-Stage Video Denoising Method' and some variants
Zhe Zheng, Gabriele Facciolo, Pablo Arias
⚠ This is a preprint. It may change before it is accepted for publication.


Recently, the field of image and video denoising has undergone a revolution thanks to deep learning approaches. These methods outperform traditional model-based approaches in almost every image/video restoration problem. In this paper, we propose an implementation of a recent approach proposed for video denoising, namely Efficient Multi-stage Video Denoising method (EMVD). The method has a lightweight and interpretable architecture consisting of three stages: temporal fusion, denoising, and refinement stages. We reproduce this method and propose three modifications aimed at improving its performance. (1) We apply motion compensation to make better use of temporal redundancy, (2) we apply variance stabilization to help this lightweight network deal with signal-dependent noise and (3) we decouple occlusion detection and fusion weights prediction. We evaluate the original method and the proposed modifications on a task of raw video denoising.