Image dehazing algorithm based on deep transfer learning and local mean adaptation.

In recent years, haze has significantly hindered the quality and efficiency of daily tasks, reducing the visual perception range. Various approaches have emerged to address image dehazing, including image enhancement, restoration, and deep learning-based dehazing methods. While these methods have improved dehazing performance to some extent, they often struggle in bright regions of the image, leading to distortions and suboptimal dehazing results. Moreover, dehazing models generally exhibit weak noise resistance, with the PSNR value of dehazed images typically falling below 30 dB. Residual noise remains in the processed images, leading to degraded visual quality. Currently, it is challenging for dehazing models to simultaneously ensure effective dehazing in bright regions while maintaining strong noise suppression capabilities. To address both issues simultaneously, we propose an image dehazing algorithm based on deep transfer learning and local mean adaptation. The framework consists of several key modules: an atmospheric light estimation module based on deep transfer learning, a transmission map estimation module utilizing local mean adaptation, a haze-free image reconstruction module, an image enhancement module, and a noise reduction module. This design ensures stable and accurate atmospheric light estimation, enabling the model to process different regions of hazy images effectively and prevent distortion artifacts. Furthermore, to enrich the details of the dehazed pictures and enhance the dehazing performance while improving the model's noise resistance, we incorporate an image enhancement module and a noise reduction module into the proposed dehazing framework. To validate the effectiveness of the proposed algorithm, we conducted dehazing experiments on a Self-Made Synthetic Hazy Dataset, the SOTS (outdoor) dataset, the NH-HAZE dataset, and O-HAZE dataset. Experimental results demonstrate that the proposed dehazing model achieves superior performance across all four datasets. The dehazed images exhibit no color distortion, and the PSNR values consistently exceed 30 dB, indicating that the dehazed images are of high quality. The dehazed images also demonstrate a significant advantage in SSIM performance compared to mainstream dehazing algorithms, consistently achieving a similarity of over 85%. This indicates that the proposed dehazing model effectively mitigates distortion while enhancing noise resistance, exhibiting strong generalization capabilities across different datasets. The experimental results confirm that the proposed dehazing algorithm handles bright regions, such as the sky, and significantly reduces residual noise in the dehazed images. Both aspects demonstrate strong performance, validating the effectiveness and superiority of the proposed dehazing model. Furthermore, the algorithm achieves consistently good dehazing performance across all three hazy datasets, demonstrating its generalization capability. This study presents a novel dehazing method and theoretical framework that can be effectively applied to scenarios such as autonomous driving and intelligent surveillance systems. The proposed model offers a novel approach to image dehazing, contributing to advancements in related fields and promoting further development in haze removal technologies.