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Dark and Bright Channel Priors for Haze Removal in Day and Night Images

U. Hari, A. Ruhan Bevi*

SRM Institute of Science and Technology, Chennai, 603203, India

* Corresponding Author: A. Ruhan Bevi. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 957-967.


Removal of noise from images is very important as a clear, denoised image is essential for any application. In this article, a modified haze removal algorithm is developed by applying combined dark channel prior and multi-scale retinex theory. The combined dark channel prior (DCP) and bright channel prior (BCP) together with the multi-scale retinex (MSR) algorithm is used to dynamically optimize the transmission map and thereby improve visibility. The proposed algorithm performs effective denoising of images considering the properties of retinex theory. The proposed method removes haze on an image scene through estimation of the atmospheric light and manipulating the transmittance parameter. The coarse transmittance is refined using edge-preserving median filtering. Experimental results depict that the proposed technique enhances the image quality degraded by foggy weather during day time or night. The results show that the proposed algorithm enhances the image quality in terms of PSNR and SSIM to a considerate level.


Cite This Article

U. Hari and A. Ruhan Bevi, "Dark and bright channel priors for haze removal in day and night images," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 957–967, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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