Dual Attention Enhanced Transformer for Image Defocus Deblurring

Image Defocus Deblurring remains a challenging problem due to the uncertainty of the blurred region and the varying depth of field. Although the convolutional neural network (CNN) has achieved promising results on this task, its limited receptive field and static weights hinder the restoration performance. In contrast, Transformer models are able to mitigate the weaknesses of CNN. However, recent Transformer-based models that deal with Image Defocus Deblurring only utilize self-attention from either spatial or channel dimension, which neglects cross-dimensional information essential for restoration. In this paper, we propose a novel Transformer model, Dual Attention Enhanced Transformer (DAEformer), for Image Defocus Deblurring. DAEformer combines self-attention from both spatial and channel dimensions, meanwhile applying auxiliary enhanced attention modules. We present Spatial Attention Enhanced Block (SAEB) and Channel Attention Enhanced Block (CAEB), which not only fuse the spatial and channel information within blocks but also enhance details. Furthermore, we design a progressive hierarchical architecture that applies SAEB/CAEB at different levels to model distinct information and facilitate fusion across blocks. Experimental results demonstrate that DAEformer can achieve state-of-the-art results on the dual-pixel dataset.