Unsupervised Vision Mamba with Contrastive Regularization Network for Image Dehazing

Benefiting from the powerful nonlinear fitting ability of neural networks, deep learning-based methods have gradually emerged as the dominate solutions for single image dehazing. However, supervised learning–based methods require paired image samples for training. To address this, an unsupervised Vision Mamba with Contrastive Regularization network (VMCR) is proposed. The network is designed based on the DisentGAN framework, and its main module is the Vision Mamba. This module performs very competitively compared to transformers, while maintaining linear time complexity and constant memory complexity with respect to the input size. Furthermore, a contrastive regularization method based on contrastive learning is proposed to enhance the reconstruction capabilities of the network and achieve superior dehazing results. Our VMCR-Net outperforms state-of-the-art unsupervised image dehazing methods, as evidenced by experimental results on several benchmarks. This research successfully proposes an enhanced unsupervised image dehazing approach, overcoming the limitations of existing methods and achieving superior dehazing performance.