HEDehazeNet: Unpaired image dehazing via enhanced haze generation

Abstract

Unpaired image dehazing models based on Cycle-Consistent Adversarial Networks (CycleGAN) typically consist of two cycle branches: dehazing-rehazing branch and hazing-dehazing branch. In these two branches, there is an asymmetry of information in the mutual transformation process between haze images and haze-free images. Previous models tended to focus more on the transformation process from haze images to haze-free images within the dehazing-rehazing branch, overlooking the provision of effective information for the formation of haze images in the hazing-dehazing branch. This oversight results in the production of haze patterns that are both monotonous and simplistic, ultimately impeding the overall performance and generalization capabilities of dehazing networks. In light of this, this paper proposes a novel model called HEDehazeNet (Dehazing Net based on Haze Generation Enhancement), which provides crucial information for the generation process of haze images through a dedicated haze generation enhancement module. This module is capable of producing three distinct modes of transmission maps - random transmission map, simulated transmission map, and mixed transmission maps combining both. Employing these transmission maps to generate hazing images with varying density and patterns provides the dehazing network with a more diverse and dynamically complex set of training samples, thereby enhancing its capacity to handle intricate scenes. Additionally, we made minor modifications to the U-Net, replacing residual blocks with multi-scale parallel convolutional blocks and channel self-attention, to further enhance the network's performance. Experiments were conducted on both synthetic and real-world datasets, substantiating the superiority of HEDehazeNet over the current state-of-the-art unpaired dehazing models.