Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network

Shunyuan Zheng, Jiamin Sun, Qinglin Liu, Yuankai Qi, Shengping Zhang; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land scenes and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose an OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset is composed of unpaired hazy and clean images taken over water. The proposed OWI-DehazeGAN learns the underlying style mapping between hazy and clean images in an encoder-decoder framework, which is supervised by a forward-backward translation consistency loss for self-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods.