Zero-Shot Image Dehazing Using Pseudo Atmospheric Light Image

Abstract

Abundant training data for deep neural networks improve the performance of single image dehazing (SID) substantially, however they suffer from the domain-shift problem of the discrepancy between the training set and the test set. In this paper, we propose a zero-shot SID method to overcome the domain-shift problem in a self-supervised learning manner. We employ two generator networks to estimate the transmission and the original scene radiance, respectively, from an input hazy image. We also synthesize the pseudo atmospheric light image (PALI) to train the transmission generator to assign zero transmission values to PALI. Since the pseudo-sky patches and the dense-hazy region rarely have the structural textures, the network learns the dense-hazy property from the PALI in a self-supervision learning manner. The experimental results show that the proposed method faithfully restores the scene radiance image, and the PALI loss is effective to train the deep neural network.