Additional Self-Attention Transformer With Adapter for Thick Haze Removal

Remote sensing images (RSIs) are widely used in the fields of geological resources monitoring, earthquake relief, and weather forecasting, but they are easily nullified due to haze cover. Transformer-based image dehazing model can better remove the haze in RSIs and improve the clarity of RSIs. However, due to the insufficient ability to extract detailed information, the model performs poorly in the case of thick haze. To solve this problem, this letter introduces an additional self-attention (AS) mechanism to help the model acquire more detailed information based on the existing Transformer-based image dehazing model and introduces an adapter module to improve the model's fitting capacity with newly added content. Experimental results on benchmark RSIs indicate that the proposed method yields an average improvement of 0.95 in peak signal-to-noise ratio (PSNR) and 0.6% in structural similarity index metrices (SSIM) for light haze removal. Notably, the method exhibits a significant enhancement of 1.34 in PSNR and 1.9% in SSIM for the removal of thick haze, underscoring its advantage in heavy haze conditions. The source code can be accessed via https://github.com/Eric3200C/ASTA.