  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.
