Open Access

ARTICLE

A Low Light Image Enhancement Method Based on Dehazing Physical Model

Wencheng Wang^1,2,*, Baoxin Yin^1,2, Lei Li^2,*, Lun Li¹, Hongtao Liu¹

1 College of Machinery and Automation, Weifang University, Weifang, 261061, China
2 College of Engineering, Qufu Normal University, Rizhao, 276827, China

* Corresponding Authors: Wencheng Wang. Email: ; Lei Li. Email:

Computer Modeling in Engineering & Sciences 2025, 143(2), 1595-1616. https://doi.org/10.32604/cmes.2025.063595

Received 18 January 2025; Accepted 25 March 2025; Issue published 30 May 2025

Abstract

In low-light environments, captured images often exhibit issues such as insufficient clarity and detail loss, which significantly degrade the accuracy of subsequent target recognition tasks. To tackle these challenges, this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis. The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image, followed by image segmentation using a quadtree method. To improve the accuracy and robustness of atmospheric light estimation, the algorithm incorporates a genetic algorithm to optimize the quadtree-based estimation of atmospheric light regions. Additionally, this method employs an adaptive window adjustment mechanism to derive the dark channel prior image, which is subsequently refined using morphological operations and guided filtering. The final enhanced image is reconstructed through the hazy image degradation model. Extensive experimental evaluations across multiple datasets verify the superiority of the designed framework, achieving a peak signal-to-noise ratio (PSNR) of 17.09 and a structural similarity index (SSIM) of 0.74. These results indicate that the proposed algorithm not only effectively enhances image contrast and brightness but also outperforms traditional methods in terms of subjective and objective evaluation metrics.

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Keywords

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