RepAr-Net: Re-Parameterized Encoders and Attentive Feature Arsenals for Fast Video Denoising

Abstract

Real-time video denoising finds applications in several fields like mobile robotics, satellite television, and surveillance systems. Traditional denoising approaches are more common in such systems than their deep learning-based counterparts despite their inferior performance. The large size and heavy computational requirements of neural network-based denoising models pose a serious impediment to their deployment in real-time applications. In this paper, we propose RepAr-Net, a simple yet efficient architecture for fast video de noising. We propose to use temporally separable encoders to generate feature maps called arsenals that can be cached for reuse. We also incorporate re-parameterizable blocks that improve the representative power of the network without affecting the run-time. We benchmark our model on the Set-8 and 2017 DAVIS-Test datasets. Our model achieves state-of-the-art results with up to 29.62% improvement in PSNR and a 50% decrease in run times over existing methods. Our codes are open-sourced at: github.com/spider-tronix/RepAr-Net.