Correcting Blue-Shift in Single-Image Dehazing via Haze-Compensated Von Kries Adaptation

Haze severely degrades image quality by reducing contrast, obscuring details, and introducing a blue-shift color cast caused by atmospheric scattering. Traditional dehazing methods, including prior-based approaches (e.g., DCP, CAP, LPMinVP) and preprocessing techniques (e.g., ICAP WB, Dynamic Gamma), improve visibility but fail to correct haze-induced color imbalance, resulting in unstable RGB distributions and unnatural tone reproduction. This study proposes the Haze-Compensated Color Von Kries (HCCVK) method, a lightweight and training-free preprocessing strategy that performs color compensation before transmission estimation in single-image dehazing. HCCVK integrates a novel red-channel compensation mechanism with Von Kries chromatic adaptation to mitigate wavelength-dependent haze suppression and stabilize chromatic consistency under varying illumination. Unlike learning-based color correction approaches, HCCVK does not require training data, is computationally efficient, and maintains algorithmic interpretability, making it suitable for practical deployment. The method was evaluated on six benchmark datasets: CHIC, Dense-Haze, I-Haze, O-Haze, SOT, and NH-Haze, covering indoor, outdoor, dense, and non-homogeneous haze scenarios. Experimental results based on the RGB color balance metric (σRGB) show that HCCVK reduces color deviation by approximately 75–92% on CHIC, 80–90% on Dense-Haze, and 82–90% on NH-Haze compared to the widely used DCP, and also outperforms CAP, ICAP WB, Dynamic Gamma, and LPMinVP by producing more compact and stable RGB distributions. These findings demonstrate that HCCVK effectively corrects blue-shift imbalance, preserves luminance consistency, and enhances the color stability of dehazing pipelines.

@article{Abdullah2025,
  title     = {Correcting Blue-Shift in Single-Image Dehazing via Haze-Compensated Von Kries Adaptation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {10},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Asniyani Nur Haidar Abdullah and Mohd Shafry Mohd Rahim and Sim Hiew Moi and Azah Kamilah Draman and Ahmad Hoirul Basori and Novanto Yudistira},
  doi       = {10.14569/IJACSA.2025.0161043},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161043}
}