Abstract
Learning and improving large language models through human preference feedback has become a mainstream approach, but it has rarely been applied to the field of low-light image enhancement. Existing low-light enhancement evaluations typically rely on objective metrics (such as FID, PSNR, etc.), which often result in models that perform well objectively but lack aesthetic quality. Moreover, most low-light enhancement models are primarily designed for global brightening, lacking detailed refinement. Therefore, the generated images often require additional local adjustments, leading to research gaps in practical applications. To bridge this gap, we propose the following innovations: 1) We collect human aesthetic evaluation text pairs and aesthetic scores from multiple low-light image datasets (e.g., LOL, LOL2, LOM, DCIM, MEF, etc.) to train a low-light image aesthetic evaluation model, supplemented by an optimization algorithm designed to fine-tune the diffusion model. 2) We propose a prompt-driven brightness adjustment module capable of performing fine-grained brightness and aesthetic adjustments for specific instances or regions. 3) We evaluate our method alongside existing state-of-the-art algorithms on mainstream benchmarks. Experimental results show that our method not only outperforms traditional methods in terms of visual quality but also provides greater flexibility and controllability, paving the way for improved aesthetic quality.
Abstract (translated)
通过人类偏好反馈来学习和改进大型语言模型已成为主流方法，但这种方法很少被应用于低光图像增强领域。现有的低光照增强评估通常依赖于客观指标（如FID、PSNR等），这往往导致在客观性能上表现良好但实际上缺乏美学质量的模型出现。此外，大多数低光照增强模型主要设计用于全局增亮，缺少细节上的精细处理。因此，生成的图像常常需要额外的局部调整，从而导致实际应用中的研究空白。 为了填补这一空白，我们提出了以下创新： 1) 我们从多个低光图像数据集（如LOL、LOL2、LOM、DCIM、MEF等）中收集了人类美学评价文本对和美学评分，以训练一个专门用于评估低光照图像美学的模型，并补充了一个旨在微调扩散模型的优化算法。 2) 我们提出了一种由提示驱动的亮度调整模块，该模块能够针对特定实例或区域执行精细的亮度和美学调整。 3) 我们在主流基准测试中与现有的最先进算法进行了比较评估。实验结果表明，我们的方法不仅在视觉质量上超越了传统方法，还提供了更大的灵活性和可控性，为提高图像美学质量铺平了道路。
URL
https://arxiv.org/abs/2503.08276
