Image dehazing using multi-scale recursive networks

Haze severely degrades the definition of images captured in outdoor scenes. The goal of image dehazing is to restore clear images from hazy ones. This problem has been significantly advanced by using deep neural networks. The performance gains mainly depend on large capacity models, which inevitably increases memory consumption and is not benefit to deployment on mobile devices. In contrast, we propose an effective image dehazing method based on a multi-scale recursive network which does not simply stack deep neural networks to improve dehazing performance. The proposed network consists of both internal and external recursions and some residual blocks. In addition, an auxiliary network is developed to collaboratively train with the primary network and guide the training process of the primary network, which is termed as the auxiliary loss. To better train the proposed network, we develop the smooth L1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_{1}$$\end{document}-norm-based content loss, perceptual loss, and auxiliary loss to regularize the proposed network. Extensive experiments demonstrate that the multi-scale recursive network achieves favorable performances against state-of-the-art image dehazing methods.