Unsupervised Neural Rendering for Image Hazing

Abstract

Image hazing aims to render a hazy image from a given clean one, which could be applied to a variety of practical applications such as gaming, filming, photographic filtering, and image dehazing. To generate plausible haze, we study two lesstouched but challenging problems in hazy image rendering, namely, i) how to estimate the transmission map from a single image without auxiliary information, and ii) how to adaptively learn the airlight from exemplars, i.e., unpaired real hazy images. To this end, we propose a neural rendering method for image hazing, dubbed as HazeGEN. To be specific, HazeGEN is a knowledge-driven neural network which estimates the transmission map by leveraging a new prior, i.e., there exists the structure similarity (e.g., contour and luminance) between the transmission map and the input clean image. To adaptively learn the airlight, we build a neural module based on another new prior, i.e., the rendered hazy image and the exemplar are similar in the airlight distribution. To the best of our knowledge, this could be the first attempt to deeply rendering hazy images in an unsupervised fashion. Comparing with existing haze generation methods, HazeGEN renders the hazy images in an unsupervised, learnable, and controllable manner, thus avoiding the labor-intensive efforts in paired data collection and the domain-shift issue in haze generation. Extensive experiments show the promising performance of our method comparing with some baselines in both qualitative and quantitative comparisons. The code will be released on GitHub after acceptance.

Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning

“…Afterwards, Li et al [24] first explore a semi-supervised dehazing framework, which can increase the network ability by using synthetic images and unlabeled real hazy images. Recently, the idea of physical-based disentanglement [23,18,55] has emerged to further increase the unpaired dehazing performance. For instance, Yang et al [50] design disentangled dehazing network (DisentGAN) to estimate the scene radiance, the medium transmission, and global atmosphere light by exploiting different generators jointly.…”