Abstract: Current algorithms often struggle with incomplete dehazing, loss of details in dark areas, and color distortion in forest foggy images. To address these issues, this paper proposed an adaptive forest image dehazing algorithm based on feature fusion attention and contrastive learning. A multi-scale feature fusion attention mechanism was designed, which dynamically adjusted feature responses by combining channel and spatial attention, thereby enhancing the representation capability of important features. A local contrast regularization module was constructed to enhance the ability of the model to discriminate variations in fog concentration in dark and distant areas. Furthermore, an adaptive color correction module was introduced to mitigate color distortion. Experimental results on both synthetic and real-world forest foggy image datasets demonstrate that the proposed algorithm outperforms existing methods, achieving significant improvements in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and a reduction in natural image quality evaluator (NIQE), and exhibiting strong robustness and generalization ability.

Key words: image dehazing, feature fusion attention, deep learning, contrastive learning