Abstract: To address the loss of detail and reduced contrast in foggy images caused by cross-layer feature mismatch between encoders and decoders in existing deep learning algorithms, an image defogging algorithm that integrates multi-scale residuals with attention mechanisms was proposed. Firstly, a multi-scale residual-aware subsampling module was designed at the encoder side. which enhanced detailed feature extraction capability through multi-branch parallel convolutions combined with residual connections and efficient channel attention mechanisms. Secondly, an adaptive fine-grained channel attention mechanism was introduced to dynamically adjust feature weights between global and local information, thereby suppressing contrast degradation. Experimental results on the RESIDE–6K, SOTS, and HSTS datasets demonstrate that the proposed algorithm outperforms six mainstream comparative algorithms in both subjective visual effects and objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while exhibiting strong generalization capability. Specifically, PSNR and SSIM values of 28.51 dB and 0.965 1 are achieved on the RESIDE−6K test set, 27.82 dB and 0.965 5 on the SOTS test set, and 29.30 dB and 0.960 1 on the HSTS test set. Through the effective integration of multi-scale residual and attention mechanisms, dual optimization of detail preservation and contrast recovery is realized, providing an effective technical solution for enhancing the quality of hazy images.