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Numerous significant progress on fisheye image rectification has beenachieved through CNN. Nevertheless, constrained by a fixed receptive field, theglobal distribution and the local symmetry of the distortion have not beenfully exploited. To leverage these two characteristics, we introducedFishformer that processes the fisheye image as a sequence to enhance global andlocal perception. We tuned the Transformer according to the structuralproperties of fisheye images. First, the uneven distortion distribution inpatches generated by the existing square slicing method confuses the network,resulting in difficult training. Therefore, we propose an annulus slicingmethod to maintain the consistency of the distortion in each patch, thusperceiving the distortion distribution well. Second, we analyze that differentdistortion parameters have their own efficacy domains. Hence, the perception ofthe local area is as important as the global, but Transformer has a weaknessfor local texture perception. Therefore, we propose a novel layer attentionmechanism to enhance the local perception and texture transfer. Our networksimultaneously implements global perception and focused local perceptiondecided by the different parameters. Extensive experiments demonstrate that ourmethod provides superior performance compared with state-of-the-art methods.

FishFormer: Annulus Slicing-based Transformer for Fisheye Rectification with Efficacy Domain Exploration