  Fisheye images are categorized fisheye into central and deviated based on the
optical center position. Existing rectification methods are limited to central
fisheye images, while this paper proposes a novel method that extends to
deviated fisheye image rectification. The challenge lies in the variant global
distortion distribution pattern caused by the random optical center position.
To address this challenge, we propose a distortion vector map (DVM) that
measures the degree and direction of local distortion. By learning the DVM, the
model can independently identify local distortions at each pixel without
relying on global distortion patterns. The model adopts a pre-training and
fine-tuning training paradigm. In the pre-training stage, it predicts the
distortion vector map and perceives the local distortion features of each
pixel. In the fine-tuning stage, it predicts a pixel-wise flow map for deviated
fisheye image rectification. We also propose a data augmentation method mixing
central, deviated, and distorted-free images. Such data augmentation promotes
the model performance in rectifying both central and deviated fisheye images,
compared with models trained on single-type fisheye images. Extensive
experiments demonstrate the effectiveness and superiority of the proposed
method.
