FishFormer: Annulus Slicing-based Transformer for Fisheye Rectification

Numerous significant progress on fisheye image rectification has been achieved through CNN. Nevertheless, constrained by a fixed receptive field, the global distribution and the local symmetry of the distortion have not been fully exploited. To leverage these two characteristics, we introduce FishFormer that processes the fisheye image as a sequence to enhance global and local perception. We tune the Transformer according to the structural properties of fisheye images. First, the uneven distortion distribution in patches generated by the existing square slicing method hinders the understanding of the global structure. Therefore, we propose an annulus slicing method to maintain the consistency of the distortion in each patch; thus, the applicability of the Transformer is expanded to perceive the distortion distribution efficiently. Second, the distortion of adjacent patches is progressive. Such explicit correlations in local regions need to be rapidly constructed and maintained, but Transformer has a weakness in local area perception. Hence, a novel layer attention mechanism is introduced to enhance the local perception and feature interaction. Our network simultaneously implements global perception and focused local perception. Extensive experiments demonstrate that our method provides superior performance compared with state-of-the-art methods.