Open Access

ARTICLE

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

by U. Hari, A. Ruhan Bevi^*

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

* Corresponding Author: A. Ruhan Bevi. Email:

Intelligent Automation & Soft Computing 2022, 34(2), 957-967. https://doi.org/10.32604/iasc.2022.023605

Received 14 September 2021; Accepted 10 November 2021; Issue published 03 May 2022

Abstract

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.

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Keywords

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