Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks for image dehazing

Abstract

Recently, Unsupervised algorithms has achieved remarkable performance in image dehazing. However, the CycleGAN framework can lead to confusion in generator learning due to inconsistent data distributions, and the DisentGAN framework lacks effective constraints on generated images, resulting in the loss of image content details and color distortion. Moreover, Squeeze and Excitation channel attention employs only fully connected layers to capture global information, lacking interaction with local information, resulting in inaccurate feature weight allocation for image dehazing. To solve the above problems, in this paper, we propose an Unsupervised Bidirectional Contrastive Reconstruction and Adaptive Fine-Grained Channel Attention Networks (UBRFC-Net). Specifically, an Unsupervised Bidirectional Contrastive Reconstruction Framework (BCRF) is proposed, aiming to establish bidirectional contrastive reconstruction constraints, not only to avoid the generator learning confusion in CycleGAN but also to enhance the constraint capability for clear images and the reconstruction ability of the unsupervised dehazing network. Furthermore, an Adaptive Fine-Grained Channel Attention (FCA) is developed to utilize the correlation matrix to capture the correlation between global and local information at various granularities promotes interaction between them, achieving more efficient feature weight assignment. Experimental results on challenging benchmark datasets demonstrate the superiority of our UBRFC-Net over state-of-the-art unsupervised image dehazing methods. This study successfully introduces an enhanced unsupervised image dehazing approach, addressing limitations of existing methods and achieving superior dehazing results. The source code is available at https://github.com/Lose-Code/UBRFC-Net.