Unsupervised Remote Sensing Haze Removal Based on Saliency-Guided Transmission Refinement

Ruohui Zheng, Libao Zhang

Abstract: Haze causes information loss and quality degradation in remote sensing images. Unsupervised learning-based dehazing methods aim to reduce reliance on paired hazy images and their labels. However, complex mapping relationships often increase the difficulty in network convergence, resulting in color distortion and loss of texture details in remote sensing images. To address these issues, we propose an unsupervised haze removal method based on saliency-guided transmission refinement for remote sensing images. Firstly, we introduce a saliency-guided transmission refinement method, which decomposes and recombines two transmission maps obtained under different conditions, guided by saliency information. Secondly, we propose a loss function comprising energy loss and texture loss. The energy loss provides an energy reference based on the coarse transmission estimation, while the texture loss enhances the preservation of texture details. Experimental results demonstrate that our method achieves comparable performance to several supervised methods.