Single Image Dehazing via Image Generating

Abstract

Outdoor images taken in bad weather conditions often suffer from poor visibility. However, single image haze removal is an ill-posed problem, because the number of the equations is smaller than the number of unknowns. In this paper, a deep learning-based method, called Dehaze CNN, is proposed to estimate a clear image patch from a hazy image patch, which can be used to reconstruct a haze-free image. Our method recovers a clear image by a learning model containing no hazy information. Our method also adopts Deep Convolution Neural Networks which takes the patch atom that can be used to generate hazy image patches and haze-free ones as the input and outputs the corresponding haze-free patch. Then we reconstruct a haze-free image from those patches. Finally, we remove the color distortion in the haze-free image via contextual regularization effectively. Experimental results show that the proposed method outperforms the state-of-the-art haze removal methods.