用遗传算法优化图像增强模型，显著提升暗光图像可视性。
Analytical-Heuristic Modeling and Optimization for Low-Light Image Enhancement
- 结合解析模型与遗传算法，优化暗光图像增强参数。
- 在LOL基准上超越26种主流方法，性能领先。
- 适合对进化计算和图像增强感兴趣的学者参考。
低光图像增强仍是开放问题，人工智能成为核心解决路径。本文提出将遗传算法（一种元启发式方法）用于优化解析模型，以改善弱光图像的可视化效果。通过融合两种解析方法与优化推理，兼顾物理合理性与计算效率，有效解决黑暗图像转为可见图像的问题。实验表明，在LOL基准测试中，该方法排名优于26种现有先进算法。结果证实，简单遗传算法配合解析推理，可在受控实验与客观对比中击败当前主流方法。本工作为群体智能与进化计算研究者开辟了新的方向，对分析与启发式推理领域具有启发意义。
原文摘要 · Abstract (English)
Low-light image enhancement remains an open problem, and the new wave of artificial intelligence is at the center of this problem. This work describes the use of genetic algorithms for optimizing analytical models that can improve the visualization of images with poor light. Genetic algorithms are part of metaheuristic approaches, which proved helpful in solving challenging optimization tasks. We propose two analytical methods combined with optimization reasoning to approach a solution to the physical and computational aspects of transforming dark images into visible ones. The experiments demonstrate that the proposed approach ranks at the top among 26 state-of-the-art algorithms in the LOL benchmark. The results show evidence that a simple genetic algorithm combined with analytical reasoning can defeat the current mainstream in a challenging computer vision task through controlled experiments and objective comparisons. This work opens interesting new research avenues for the swarm and evolutionary computation community and others interested in analytical and heuristic reasoning.
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