LAUSR

Abstract Single image dehazing is one of the most challenging and important tasks in computer vision and image processing. In this paper, we propose a Non-local Dehazing Network (NLDN), which learns the mapping between hazy images and haze-free images. Our network architecture consists three components: the first is full point-wise convolutional part, which extracts Non-local statistical regularities; the second is feature combination part, which learns the spatial relation of statistical regularities; the third is reconstruction part, which recovers the haze-free image by the features extracted from the second part. By using these three components, we obtain a high quality dehazing result. Experimental results show that our method performs favorably against other state-of-the-art methods on both synthetic dataset and real-world images.