Hazy Remote Sensing Image Restoration Based on Saliency-Guided Transmission Optimization and Texture Boosting
Yanmeng Liu, Libao Zhang · 2023
Remote sensing images (RSIs) are susceptible to haze, losing the spectral fidelity and texture details. Haze removal is highly desired in the follow-up tasks such as target identification and semantic segmentation. Most previous works adopted a unified dehazing method to process the entire image and could not fully restore the rich texture information contained in RSIs. In this paper, we propose a hazy RSI restoration method based on saliency-guided transmission optimization and texture boosting. First, we design a saliency-guided transmission optimization method, which achieves different degrees of dehazing to areas with different saliency, fully obtaining the texture and spectral information of RSIs. Second, we propose a saliency-guided atmospheric light (AL) correction method, which fuses the AL of salient regions and non-salient regions to avoid excessive energy attenuation. Finally, a saliency-guided RSI texture boosting method is introduced, further enhancing the texture details of the dehazed RSIs. The efficiency of the proposed method is validated by comparing its performance with six state-of-art schemes.