Weakly supervised single image dehazing

Abstract Single image dehazing is a critical image pre-processing step for many practical vision systems. Most existing dehazing methods solve this problem utilizing various of hand-crafted priors or by supervised training on the synthetic hazy image information (such as haze-free image, transmission map and atmospheric light). However, the assumptions on the hand-crafted priors are easily violated and collecting realistic transmission map and atmospheric light are unpractical. In this paper, we propose a novel weakly supervised network based on the multi-level multi-scale block. The proposed network reduces the constraint on the training data and automatically estimates the transmission map and the atmospheric light as well as the intermediate haze-free image without using any realistic transmission map and atmospheric light as supervision. Moreover, the estimated intermediate haze-free image helps to generate accurate transmission map and atmospheric light by embedding the physical-model, which presents reliable restoration of the final haze-free image. In particular, our network also can be trained on the real-world dataset to fine-tune the model and the fine-tuning operation improves the dehazing performance on the real-world dataset. Quantitative and qualitative experimental results demonstrate the proposed method performs on par with the supervised methods.