Abstract
Most existing low-light image enhancement (LLIE) methods rely on pre-trained model priors, low-light inputs, or both, while neglecting the semantic guidance available from normal-light images. This limitation hinders their effectiveness in complex lighting conditions. In this paper, we propose VLM-IMI, a novel framework that leverages large vision-language models (VLMs) with iterative and manual instructions (IMIs) for LLIE. VLM-IMI incorporates textual descriptions of the desired normal-light content as enhancement cues, enabling semantically informed restoration. To effectively integrate cross-modal priors, we introduce an instruction prior fusion module, which dynamically aligns and fuses image and text features, promoting the generation of detailed and semantically coherent outputs. During inference, we adopt an iterative and manual instruction strategy to refine textual instructions, progressively improving visual quality. This refinement enhances structural fidelity, semantic alignment, and the recovery of fine details under extremely low-light conditions. Extensive experiments across diverse scenarios demonstrate that VLM-IMI outperforms state-of-the-art methods in both quantitative metrics and perceptual quality. The source code is available at this https URL.
Abstract (translated)
大多数现有的低光图像增强（LLIE）方法依赖于预训练的模型先验、低光照输入或两者兼有，而忽略了正常光线图像提供的语义指导。这种限制阻碍了它们在复杂照明条件下的有效性。在这篇论文中，我们提出了VLM-IMI，这是一种新颖的框架，利用大型视觉语言模型（VLM）和迭代手动指令（IMIs）来进行低光图像增强。VLM-IMI整合了所需正常光照内容的文字描述作为增强提示，使语义引导的恢复成为可能。为了有效融合跨模态先验知识，我们引入了一个指令先验融合模块，该模块动态地对齐和融合图像与文本特征，促进生成详细且语义一致的输出。在推理过程中，我们采用迭代手动指令策略来优化文字说明，逐步提升视觉质量。这种细化增强了结构保真度、语义一致性，并改善了极低光照条件下的细节恢复能力。广泛的跨场景实验表明，VLM-IMI在定量指标和感知质量方面都优于最先进的方法。源代码可在提供的链接中获取。
URL
https://arxiv.org/abs/2507.18064
