Remote Sensing Image Uneven Haze Removal Based On Haze Density Estimation and Saliency-Driven Dual Channel Fusion

Remote sensing images (RSIs) are usually degraded by haze, losing the spectral fidelity and texture details. Most previous dehazing works adopted a unified dehazing strategy to process the entire image, ignoring the discrepancies of haze density, spectral information and texture complexity among different regions in one RSI, and thus resulted in the distorted spectral and blurred texture. In this paper, we propose a skip-connected network based on haze density estimation and saliency-driven dual channel fusion, realizing a differentiated defogging method. First, we propose a haze density estimation model, generating the haze density map. Second, we design a saliency-driven dual channel fusion to distinguish the spectral and texture features of different regions in the input, generating the saliency map. The two maps above jointly serve as guidance for the network to perform differentiated dehazing. Finally, an attention-based skip-connected block is introduced, which helps to fuse multi-scale information and obtain more refined dehazing results. A mixed loss function is also constructed to retain more texture details. The efficiency of the proposed method is validated by comparing its performance with other SOTA schemes.