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In recent years, Transformer networks are beginning to replace pureconvolutional neural networks (CNNs) in the field of computer vision due totheir global receptive field and adaptability to input. However, the quadraticcomputational complexity of softmax-attention limits the wide application inimage dehazing task, especially for high-resolution images. To address thisissue, we propose a new Transformer variant, which applies the Taylor expansionto approximate the softmax-attention and achieves linear computationalcomplexity. A multi-scale attention refinement module is proposed as acomplement to correct the error of the Taylor expansion. Furthermore, weintroduce a multi-branch architecture with multi-scale patch embedding to theproposed Transformer, which embeds features by overlapping deformableconvolution of different scales. The design of multi-scale patch embedding isbased on three key ideas: 1) various sizes of the receptive field; 2)multi-level semantic information; 3) flexible shapes of the receptive field.Our model, named Multi-branch Transformer expanded by Taylor formula(MB-TaylorFormer), can embed coarse to fine features more flexibly at the patchembedding stage and capture long-distance pixel interactions with limitedcomputational cost. Experimental results on several dehazing benchmarks showthat MB-TaylorFormer achieves state-of-the-art (SOTA) performance with a lightcomputational burden. The source code and pre-trained models are available athttps://github.com/FVL2020/ICCV-2023-MB-TaylorFormer.

MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing