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.

利用量化的先验知识和图像精修方法，该文提出了一种名为 CodeEnhance 的新型低光图像增强方法，通过学习从低光图像到离散码书的映射，改善图像恢复中的不确定性和噪声压制造成的纹理和颜色信息损失，并结合语义信息、码书位移和交互特征转换模块，实现了对低光图像的交互式增强。实验证明，CodeEnhance 在质量和保真度上显著提高了低光图像增强的性能，对于不均匀照明、噪声和颜色失真等各种退化具有较强的鲁棒性。