Open Access

ARTICLE

An Advanced Bald Eagle Search Algorithm for Image Enhancement

Pei Hu¹, Yibo Han¹, Jeng-Shyang Pan^2,3,*

1 School of Computer and Software, Nanyang Institute of Technology, Nanyang, 473004, China
2 College of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, 210044, China
3 Department of Information Management, Chaoyang University of Technology, Taichung, 413310, Taiwan

* Corresponding Author: Jeng-Shyang Pan. Email:

Computers, Materials & Continua 2025, 82(3), 4485-4501. https://doi.org/10.32604/cmc.2024.059773

Received 16 October 2024; Accepted 11 December 2024; Issue published 06 March 2025

Abstract

Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images. This paper approaches the topic as an optimization problem and uses the bald eagle search (BES) algorithm to achieve optimal results. In our proposed model, gamma correction and Retinex address color cast issues and enhance image edges and details. The final enhanced image is obtained through color balancing. The BES algorithm seeks the optimal solution through the selection, search, and swooping stages. However, it is prone to getting stuck in local optima and converges slowly. To overcome these limitations, we propose an improved BES algorithm (ABES) with enhanced population learning, position updates, and control parameters. ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality, and the maximization of information entropy is utilized as the objective function. Real benchmark images are collected to validate its performance. Experimental results demonstrate that ABES outperforms the existing image enhancement methods, including the flower pollination algorithm, the chimp optimization algorithm, particle swarm optimization, and BES, in terms of information entropy, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and patch-based contrast quality index (PCQI). ABES demonstrates superior performance both qualitatively and quantitatively, and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.

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Keywords

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