Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations; (2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion.

翻译：低光图像增强（LLIE）旨在改善低照度图像。然而，现有方法面临两大挑战：（1）由多样化亮度退化导致的不确定性恢复问题；（2）噪声抑制与亮度增强过程中导致的纹理与色彩信息丢失。本文提出一种名为CodeEnhance的新型增强方法，通过利用量化先验与图像细化来解决上述挑战。具体而言，我们将LLIE重新定义为从低光图像到离散码本的图像-编码映射学习任务，该码本基于高质量图像预训练而成。为优化该过程，引入语义嵌入模块（SEM）以融合语义信息与底层特征，并设计码本偏移机制（CS），使预训练码本适应低光数据集的独特特性。此外，我们提出交互式特征变换模块（IFT），在图像重建过程中精炼纹理与色彩信息，支持根据用户偏好进行交互式增强。在真实与合成基准数据集上的大量实验表明，先验知识与可控信息传输的融合显著提升了LLIE在质量与保真度方面的性能。所提出的CodeEnhance对不均匀光照、噪声及色彩失真等多种退化现象展现出优异的鲁棒性。