Efficiently Exploiting Spatially Variant Knowledge for Video Deblurring

Qian Xu, Xiaobin Hu, Donghao Luo, Ying Tai, Chengjie Wang, Yuntao Qian

Abstract: Video deblurring is a challenging task as the blur is often spatially variant. Existing methods mainly engage in building the spatial-temporal correspondence among the frames. As one of the widely-used frameworks, the long-range temporal propagation usually suffers from the expensive computation cost and error accumulation caused by the numerous connections among temporal frames. Meanwhile, the exploration of spatial-variant information from the neighbor frames is often ignored in video deblurring. To tackle these issues, we tailor an efficient short-range multi-scale framework slimming the long-range propagation and exploiting the most relevant neighbor temporal knowledge. For capturing spatial knowledge, we further propose a spatial feature extractor, named the spatially variant adaptive block, to adaptively generate the location-wise kernel to cater to the spatially variant character of blur. For efficient temporal exploitation, a simple inter-frame shift as a motion compensation is developed to avoid expensive long temporal relevance modeling. Both quantitative and qualitative evaluation results on benchmark datasets demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.

External IDs:dblp:journals/tcsv/XuHLTWQ24