In order to solve the problem of blurring of images affected by foggy days, an improved de-fogging algorithm founded upon AOD-Net is designed to address the challenges with color difference and unclear texture of details in images after de-fogging by AOD-Net image de-fogging algorithm. Firstly, the CBAM module is introduced to improve the network’s ability to extract both local and global characteristics of the input imagery through its attention mechanism, while decreasing the computational load and improving the robustness of the model; then the Sparse feature reactivation (SFR) module is added to boost the effectiveness of feature reuse and enhance the quality of the model for defogging; and finally, the hybrid loss function of MS-SSIM loss function and L1 loss function is used to improve the contrast and brightness of the defogged image. contrast, brightness and color saturation of the defogged image. The findings indicate that the defogged images from the improved algorithm exhibit superior performance metrics of peak signal-to-noise ratio and structural similarity index, which are crucial for improving image quality, enhancing the performance of the visual system, and broadening its application in various fields, with the potential impact of significantly improving the productivity of the society and the quality of life.