A PROGRESSIVE IMAGE DEHAZING FRAMEWORK WITH INTER AND INTRA CONTRASTIVE LEARNING

Image dehazing, aims to estimate latent haze-free images from hazy images, suffering from a lot of lost information. Existing contrastive learning methods tend to utilize haze-free images as positive samples without consideration of negative samples. Even if negative samples are employed, the connection between patches within an image is always ignored. In addition, it is hard to train end-to-end dehazing networks due to the enormous gap between hazy images and corresponding clear images. In this paper, we propose a novel progressive image dehazing framework with inter and intra contrastive learning to solve the above problems. Specifically, the Inter and Intra Contrastive Learning (IICL) is proposed, in which the brightest and darkest patches within the same image are considered for contrastive learning. Furthermore, a progressive image dehazing framework consisting of an efficient Pre-restore Module (PRM) and an Alternative Restored Module (ARM) is proposed to facilitate the end-to-end model training. It is noted that our framework can be a complement to existing image dehazing methods. Extensive experiments on the dehazing benchmark demonstrate that our framework benefits various dehazing models which surpass previous state-of-the-art image dehazing methods.