  Distortion rectification is often required for fisheye images. The
generation-based method is one mainstream solution due to its label-free
property, but its naive skip-connection and overburdened decoder will cause
blur and incomplete correction. First, the skip-connection directly transfers
the image features, which may introduce distortion and cause incomplete
correction. Second, the decoder is overburdened during simultaneously
reconstructing the content and structure of the image, resulting in vague
performance. To solve these two problems, in this paper, we focus on the
interpretable correction mechanism of the distortion rectification network and
propose a feature-level correction scheme. We embed a correction layer in
skip-connection and leverage the appearance flows in different layers to
pre-correct the image features. Consequently, the decoder can easily
reconstruct a plausible result with the remaining distortion-less information.
In addition, we propose a parallel complementary structure. It effectively
reduces the burden of the decoder by separating content reconstruction and
structure correction. Subjective and objective experiment results on different
datasets demonstrate the superiority of our method.
