Contrast enhancement of digital images using dragonfly algorithm

• Published in: Automatika Vol. 65; no. 4; pp. 1545 - 1557
• Main Authors: Saha, Soumyajit, Chatterjee, Somnath, Sen, Shibaprasad, Oliva, Diego, Perez-Cisneros, Marco, Sarkar, Ram
• Format: Journal Article Paper
• Language: English
• Published: Ljubljana Taylor & Francis Ltd 01.10.2024; KoREMA - Hrvatsko društvo za komunikacije,računarstvo, elektroniku, mjerenja i automatiku; Taylor & Francis Group
• Subjects: Ablation; Algorithms; Digital imaging; Dragonfly algorithm; Image acquisition; Image contrast; image contrast enhancement; Image enhancement; Image quality; Kodak dataset; meta-heuristic; Parameter modification; Signal to noise ratio
• ISSN: 0005-1144; 1848-3380; 1848-3380
• DOI: 10.1080/00051144.2024.2404365

Contrast enhancement aims to amplify the visual quality of images by modifying the contrast level because digital images may get distorted by casual acquisition. The article deals with contrast enhancement as an optimization problem and uses the Dragonfly Algorithm (DA) to find the optimal grey-level intensity values. The DA for contrast enhancement uses five control parameters (entropy, number of edges, total intensities of edges, the variance of the probability of occurrence of each grey value, and the number of grey levels) to generate an objective function. An ablation study is also performed to understand how different controlling parameter combinations contribute to determining the optimal solution. The proposed approach considers 24 grey-scale images from the Kodak dataset and metrics as Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity (VIF), and Structural Similarity Index Measure (SSIM) to verify the output's performance. The PSNR, VIF, and SSIM values in the experiments are 30.87, 0.7451, and 0.9523, respectively. The experimental observations reveal that the proposed DA-based image contrast enhancement produces high-quality images from its low-contrast counterparts. Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this Github repository.