A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift

Understand

Video restoration, which aims to restore clear frames from degraded videos, has numerous important applications.

• The key to video restoration depends on utilizing inter-frame information.
• However, existing deep learning methods often rely on complicated network architectures, such as optical flow estimation, deformable convolution, and cross-frame self-attention layers, resulting in high computational costs.
• In this study, we propose a simple yet effective framework for video restoration.