Residual multi-scale pixel attention fusion network for image deblurring

Abstract

The applications of multi-scale fusion strategy to feature are very effective and common in image restoration. However, there is a lack of research on the application of the multi-scale fusion strategy to attention mechanism, although attention mechanism has also been verified to be effective in image restoration. To address this problem, we propose a residual multi-scale pixel attention fusion block (RMPAFB) to refine the input feature, which successfully combine the multi-scale fusion strategy with pixel attention. RMPAFB can capture feature correspondences from multi-scale pixel attention map, which can be more effective for feature refinement than single-scale pixel attention map. Based on RMPAFB, we build an efficient and effective network called RMPAFNet for image deblurring. Substantial experiments on several benchmark datasets have showed that multi-scale pixel attention performs better than single-scale pixel attention and our proposed RMPAFNet achieves state-of-the-art performance while requiring fewer overheads than recent competing deblurring models.