  Fisheye image rectification has a long-term unresolved issue with
synthetic-to-real generalization. In most previous works, the model trained on
the synthetic images obtains unsatisfactory performance on the real-world
fisheye image. To this end, we propose a Dual Diffusion Architecture (DDA) for
the fisheye rectification with a better generalization ability. The proposed
DDA is simultaneously trained with paired synthetic fisheye images and
unlabeled real fisheye images. By gradually introducing noises, the synthetic
and real fisheye images can eventually develop into a consistent noise
distribution, improving the generalization and achieving unlabeled real fisheye
correction. The original image serves as the prior guidance in existing DDPMs
(Denoising Diffusion Probabilistic Models). However, the non-negligible
indeterminate relationship between the prior condition and the target affects
the generation performance. Especially in the rectification task, the radial
distortion can cause significant artifacts. Therefore, we provide an
unsupervised one-pass network that produces a plausible new condition to
strengthen guidance. This network can be regarded as an alternate scheme for
fast producing reliable results without iterative inference. Compared with the
state-of-the-art methods, our approach can reach superior performance in both
synthetic and real fisheye image corrections.
