Unsupervised Multi-Branch Network with High-Frequency Enhancement for Image Dehazing

Abstract

Recently, CycleGAN-based methods have been widely applied to the unsupervised image dehazing and achieved significant results. However, most existing CycleGAN-based methods ignore that the input of the generator contains two different distributions of data which can often lead to confusion in the learning process of the generator, consequently limiting the final dehazing performance. Moreover, reconstructing clear images through model architecture design and loss functions is an indirect constraint, making it difficult to compensate for the missing high-frequency information, such as textures and structures in the extracted features from hazy images. To address these issues, in this paper, we propose an Unsupervised Multi-Branch with High-Frequency Enhancement Network (UME-Net) which contain an Multi-Branch Dehazing Network (MBDN) and a High-Frequency Components Enhancement Module (HFEM). Specifically, MBDN constructs a single unsupervised dehazing network with Shared Encoding Module (SEM) and Multi-Branch Decoding Module (MDM). SEM enhance the consistency of feature representation and MDM effectively addresses the confusion during the generator learning process in CycleGAN-based methods. Furthermore, based on a key observation that hazy images and their corresponding clear images exhibit only subtle differences in high-frequency information, the HFEM is designed to compensates for the missing high-frequency information in the network which further enhances the reconstruction capability of the UME-Net for restore edge and texture information in obscured by dense haze. Experimental results on challenging benchmark datasets demonstrate the superiority of our UME-Net over SOTA unsupervised image dehazing methods. The source code is available at https://www.github.com/thislzm/UME-Net.

Keywords: Image dehazing, Physical model, unsupervised learning, Multi-Branch Network, High-frequency enhancement