Flow-Guided Sparse Transformer for Video Deblurring

Understand

Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring.

• However, CNN-based methods show limitations in capturing long-range dependencies and modeling non-local self-similarity.
• In this paper, we propose a novel framework, Flow-Guided Sparse Transformer (FGST), for video deblurring.
• In FGST, we customize a self-attention module, Flow-Guided Sparse Window-based Multi-head Self-Attention (FGSW-MSA).