Dual Prior Learning for Blind and Blended Image Restoration.

Abstract

Unsupervised single image restoration approach, Deep Image Prior (DIP), aims to restore images by learning enough raw image statistic priors from the corrupted observation. However, it is not uncommon that an image is contaminated by the multiple unknown distortions. Thus it is hard to disentangle the clean and the hybrid distortion signals by solely relying on image prior learning to restore the images. To overcome this problem, we propose the Dual Prior Learning (DPL) method by taking both image and distortion priors into account. DPL goes beyond DIP by considering an additional step to explicitly learn the blended distortion prior. Furthermore, to coordinate the learning of two priors and avoid them learning the same knowledge, we exploit unpaired training data to enforce a weakly supervision in an adversarial manner to encourage disentangling two priors. Extensive experiments show the effectiveness and appealing performance of the proposed DPL on restoring images with challenging unknown blended distortions.