Exploiting Temporal Attention Features for Effective Denoising in Videos

TL;DR

This paper introduces a two-stage video denoising method leveraging temporal and spatial attention mechanisms to improve denoising quality while reducing flickering artifacts.

Contribution

It proposes a novel Spatio-TemporalNetwork architecture with channel-wise soft attention for enhanced video denoising performance.

Findings

Effective reduction of flickering in denoised videos

Improved denoising quality over traditional methods

Utilization of attention mechanisms enhances feature extraction

Abstract

Video Denoising is one of the fundamental tasks of any videoprocessing pipeline. It is different from image denoising due to the tem-poral aspects of video frames, and any image denoising approach appliedto videos will result in flickering. The proposed method makes use oftemporal as well as spatial dimensions of video frames as part of a two-stage pipeline. Each stage in the architecture named as Spatio-TemporalNetwork uses a channel-wise attention mechanism to forward the encodersignal to the decoder side. The Attention Block used in this paper usessoft attention to ranks the filters for better training.