Exploiting Temporal Attention Features for Effective Denoising in Videos

2020/08/05 by Aryansh Omray, Samyak Jain, Omray, Aryansh +5
Computer Science · #Advanced Data Compression Techniques #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2008.02344

openalex publication_date 2020/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

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.

Citations

• Adam: A Method for Stochastic Optimization
• Long Short-Term Memory
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder
• Deep Residual Learning for Image Recognition
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

Related

• Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image Denoisers
• First image then video: A two-stage network for spatiotemporal video denoising
• Space-Time Attention with Shifted Non-Local Search
• Is Space-Time Attention All You Need for Video Understanding?
• Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network