

















Abstract:Low-light images suffer from poor visibility, noise, and color distortion. Existing Retinex-based enhancement methods rely on manually tuned parameters that do not generalize across different lighting conditions. This paper proposes BFORE (Butterfly-Firefly Optimized Retinex Enhancement), a framework that automatically finds the best enhancement parameters for each image. BFORE works in two phases: (1) a Butterfly Optimization Algorithm (BOA) searches for optimal Multi-Scale Retinex with Color Restoration (MSRCR) parameters, then (2) a Firefly Algorithm (FA) fine-tunes gamma correction, denoising, and color parameters. Both phases maximize a Gaussian Naturalness Score (GNS), a no-reference metric that measures how natural the enhanced image looks. Standard quality metrics (PSNR, SSIM, NIQE) are computed only after optimization, ensuring zero data leakage. On 30 synthetic image pairs, BFORE achieves GNS = 0.971, outperforming the next-best method MSRCR (0.894) by 8.6%. On 115 real images from the LOL dataset, BFORE achieves GNS = 0.887, outperforming MSRCR (0.808) by 9.8%. A controlled comparison with three deep learning baselines (Zero-DCE, SCI, IAT) trained under identical conditions shows BFORE surpasses the best DL method by 14.7% in GNS. An ablation study confirms that the hybrid BOA+FA strategy significantly outperforms each optimizer in isolation, and a scalability analysis at three evaluation budgets shows that the structured optimizer significantly outperforms uniform random sampling once compute is available (p = 0.009 at 128 evaluations, p = 0.021 at 300 evaluations). All improvements are statistically significant (p < 0.0001, Wilcoxon signed-rank test). Processing time is 3-6 minutes per image on CPU, suitable for offline applications.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| MSC classes: | 68U10, 90C59 |
| ACM classes: | I.4.3; I.4.9 |
| Cite as: | arXiv:2605.03509 [cs.CV] |
| (or arXiv:2605.03509v4 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.03509 arXiv-issued DOI via DataCite |
From: Ahmed Cherif [view email]
[v1]
Tue, 5 May 2026 08:43:50 UTC (26 KB)
[v2]
Sat, 9 May 2026 09:52:29 UTC (1 KB) (withdrawn)
[v3]
Fri, 15 May 2026 16:14:10 UTC (30 KB)
[v4]
Sat, 23 May 2026 09:28:17 UTC (1,463 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。