To address the issues of inaccurate atmospheric light estimation, low color saturation, and dim brightness in highlight regions after dehazing using dark channel prior-based methods, this paper proposes a novel dehazing approach based on superpixel segmentation and dark-bright channel fusion. This method utilizes superpixel segmentation to refine the segmentation of hazy images, clustering regions with similar depth-of-field characteristics into superpixel blocks, using these blocks to replace traditional fixed filtering windows, which effectively suppresses the blocking effect in areas of gradient abruptness. Through adaptive threshold segmentation, the algorithm identifies highlight and dark regions, and utilizes a hybrid dark channel strategy to enhance the robustness of the dehazing algorithm across various scenes. We construct an atmospheric light estimation model with joint constraints from dark and bright channels, and apply guided filtering for refinement, improving both the accuracy and spatial consistency of atmospheric light estimation. Based on optimized transmission and atmospheric light parameters, fog-free images are derived through inverse calculation using the atmospheric scattering model. Experimental results demonstrate that the proposed method achieves a PSNR of 26.815 dB on the OTS dataset, an SSIM of 0.576 on the O-HAZE dataset, and requires only 36.281 s of processing time on the I-HAZE dataset, with average improvements of 13% and 5% in PSNR and SSIM, respectively. The proposed method effectively mitigates the persistent challenges of low saturation and insufficient luminance in highlight regions of reconstructed images, with both objective metrics and subjective evaluations demonstrating superior performance compared to state-of-the-art algorithms.