A Model-Driven Deep Dehazing Approach by Learning Deep Priors

Photos taken in hazy weather are usually covered with white masks and lose important details. Haze removal is a fundamental task and a prerequisite to many other vision tasks. Single image dehazing is an ill-posed inverse problem that has attracted much attention in recent years. Generally, current single dehazing methods can be categorized into the traditional prior-based methods and the data-driven deep learning methods that respectively investigate haze-related image priors and deep architectures. In this paper, we propose a novel model-driven deep learning approach that combines the advantages of both kinds of methods. First, we build an energy model for single image dehazing with physical constraints in both color image space and haze-related feature space (implemented as dark channel space in this work), regularized by haze-related image priors. Then, we design an iterative optimization algorithm for solving the proposed dehazing energy model based on the half-quadratic splitting algorithm, and the priors are transformed to their corresponding proximal operators. Finally, inspired by the optimization algorithm, we design a deep dehazing neural network, dubbed as proximal dehaze-net, by learning the proximal operators for haze-related image priors using CNNs. Our network incorporates physical model constraints of hazes and haze-related prior learning into a novel deep architecture. Extensive experiments show that our method achieves promising performance for single image dehazing.