Image dehazing algorithm based on light-value weighted allocation and multi-layer restricted perception

In image dehazing, the dehazing performance in bright regions and the model's robustness to noise are critical evaluation criteria. However, existing dehazing models often suffer from distortions in the bright areas and exhibit weak noise resistance. We propose an image dehazing algorithm based on light-value weighted allocation and multi-layer restricted perception (DWARP) to address these issues. The proposed algorithm first constructs an atmospheric light estimation module based on weighted allocation. It reduces the initial estimation error by zeroing out overexposed pixel values, then identifying key factors affecting atmospheric light estimation and assigning weights accordingly. Finally, a threshold-restricted adjustment is applied to the estimated result, achieving a three-stage refinement in atmospheric light estimation accuracy. Secondly, by computing the universal range of transmittance for both bright and non-bright regions, a multi-layer restricted perception scheme for transmittance is designed. This approach transforms the distortion issue in bright regions into a problem of reducing transmittance estimation errors. Finally, to enhance the visual quality of the dehazed image and improve the model's noise robustness, a brightness adjustment module and a Gaussian denoising module are embedded into the dehazing model. Experimental results demonstrate that the DWARP algorithm effectively prevents distortion in the dehazing process for bright regions and enhances the model's noise robustness. The DWARP algorithm achieved an average PSNR of 37.41 dB, an average SSIM of 88.74%, and an average VIF of 0.89 across four datasets, with an average dehazing time of 0.633 s. Compared to the RIDCP algorithm, DWARP improved the PSNR by an average of 6.63 dB, SSIM by 2.18%, and VIF by 0.03 while enhancing dehazing efficiency by an average of 0.055 s. To validate the effectiveness of the DWARP algorithm, we conducted dehazing experiments on four foggy datasets, demonstrating the proposed algorithm's effectiveness and superiority. The DWARP algorithm not only effectively processes bright regions, such as the sky, in foggy images but also exhibits strong noise resistance, validating the scientific and theoretical correctness of the proposed improvements. This dehazing model provides a novel approach for fog removal in fields such as intelligent transportation, contributing to the advancement and development of these areas.