RI-GAN: An End-To-End Network for Single Image Haze Removal

Abstract

The presence of the haze or fog particles in the atmosphere causes visibility degradation in the captured scene. Most of the initial approaches anticipate the transmission map of the hazy scene, airlight component and make use of an atmospheric scattering model to reduce the effect of haze and to recover the haze-free scene. In spite of the remarkable progress of these approaches, they propagate cascaded error upstretched due to the employed priors. We embrace this observation and designed an end-to-end generative adversarial network (GAN) for single image haze removal. Proposed network bypasses the intermediate stages and directly recovers the haze-free scene. Generator architecture of the proposed network is designed using a novel residual inception (RI) module. Proposed RI module comprises of dense connections within the multi-scale convolution layers which allows it to learn the integrated flavors of the haze-related features. Discriminator of the proposed network is built using the dense residual module. Further, to preserve the edge and the structural details in the recovered haze-free scene, structural similarity index and edge loss along with the L1 loss are incorporated in the GAN loss. Experimental analysis has been carried out on NTIRE2019 dehazing challenge dataset, D-Hazy [1] and indoor SOTS [22] databases. Experiments on the publically available datasets show that the proposed method outperforms the existing methods for image de-hazing.