Abstract
In this paper, we present an algorithm to directly restore
a clear image from a hazy image. This problem is highly illposed and most existing algorithms often use hand-crafted
features, e.g., dark channel, color disparity, maximum contrast, to estimate transmission maps and then atmospheric
lights. In contrast, we solve this problem based on a conditional generative adversarial network (cGAN), where the
clear image is estimated by an end-to-end trainable neural network. Different from the generative network in basic cGAN, we propose an encoder and decoder architecture
so that it can generate better results. To generate realistic
clear images, we further modify the basic cGAN formulation by introducing the VGG features and an L1-regularized
gradient prior. We also synthesize a hazy dataset including indoor and outdoor scenes to train and evaluate the
proposed algorithm. Extensive experimental results demonstrate that the proposed method performs favorably against
the state-of-the-art methods on both synthetic dataset and
real world hazy images