Exploiting Temporal Attention Features for Effective Denoising in Videos
This work addresses video denoising for video processing applications, but it appears incremental as it builds on existing attention mechanisms.
The paper tackles video denoising by proposing a two-stage spatio-temporal network with channel-wise attention to address flickering issues from image-based methods, achieving competitive results on benchmark datasets.
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.