Single Remote Sensing Image Haze Removal Based on Spatial and Spectral Self-Adaptive Model

Remote sensing images are extensively applied in various fields, however, they usually suffer from haze pollution, which always leads to low contrast and color distortion. In this paper, we propose a novel and effective spatial and spectral self-adaptive haze removal model for remote sensing images. Our research is based on the dark channel prior, meanwhile, we ameliorate the prior in two aspects as follow: first, to remove uneven haze in remote sensing images, we modify the original constant \( \upomega \) to a matrix, and the value of \( \upomega \) changes with density of haze, so the processing intensity varies as haze density changes. Second, the dark channel prior has severe color distortion when dealing with bright landforms, to solve this problem, we separate these bright areas automatically from other landforms and handle them alone. Experimental results reveal that our proposed model is able to remove uneven haze and keep good color consistency when dealing with bright landforms. Both visual effect and quantitative assessment demonstrate that the proposed algorithm is effective.