ViStripformer: A Token-Efficient Transformer for Versatile Video
  Restoration
Fu-Jen Tsai Yan-Tsung Peng Chen-Yu Chang Chan-Yu Li Yen-Yu Lin Chung-Chi Tsai Chia-Wen Lin
Video restoration is a low-level vision task that seeks to restore clean,
sharp videos from quality-degraded frames. One would use the temporal
information from adjacent frames to make video restoration successful.
Recently, the success of the Transformer has raised awareness in the
computer-vision community. However, its self-attention mechanism requires much
memory, which is unsuitable for high-resolution vision tasks like video
restoration. In this paper, we propose ViStripformer (Video Stripformer), which
utilizes spatio-temporal strip attention to catch long-range data correlations,
consisting of intra-frame strip attention (Intra-SA) and inter-frame strip
attention (Inter-SA) for extracting spatial and temporal information. It
decomposes video frames into strip-shaped features in horizontal and vertical
directions for Intra-SA and Inter-SA to address degradation patterns with
various orientations and magnitudes. Besides, ViStripformer is an effective and
efficient transformer architecture with much lower memory usage than the
vanilla transformer. Extensive experiments show that the proposed model
achieves superior results with fast inference time on video restoration tasks,
including video deblurring, demoireing, and deraining.