Abstract Haze reduces visibility in outdoor images by lowering contrast and obscuring scene details. This degradation undermines the reliability of practical vision systems. Prior-based dehazing methods often struggle in highlight-dominant scenes because atmospheric-light estimation can be corrupted by non-sky bright regions. To address this issue, we propose a highlight-aware image dehazing framework based on sky-feature-constrained atmospheric-light estimation and its real-time ZYNQ implementation. The method identifies reliable sky cues from the dark channel using brightness consistency, texture flatness, color uniformity, and spatial location, and then performs adaptive transmission estimation, radiance recovery, and gamma correction within the dark-channel-prior framework. A PS-PL co-design on ZYNQ7020 is further developed for real-time video dehazing. On the SOTS-Outdoor benchmark, the proposed method achieves 19.68 dB PSNR and 0.8196 SSIM, outperforming the classical DCP baseline by 3.62 dB in PSNR, while the deployed system reaches 108 FPS on ZYNQ7020. These results indicate that the proposed framework provides a practical solution for scene-adaptive and deployable dehazing in adverse outdoor environments.
Zhao et al. (2026) studied this question.