CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Xu Wu, XianXu Hou, Zhihui Lai, Jie Zhou, Ya-nan Zhang, Witold Pedrycz, Linlin Shen

TL;DR
CodeEnhance introduces a novel codebook-driven method for low-light image enhancement, leveraging semantic embedding, adaptive codebook shifting, and interactive feature transformation to improve image quality and robustness against various degradations.
Contribution
It reframes LLIE as an image-to-code learning task using quantized priors, and introduces modules for semantic integration, codebook adaptation, and interactive refinement, which are novel contributions.
Findings
Outperforms existing methods on real-world and synthetic benchmarks.
Demonstrates robustness to noise, uneven illumination, and color distortions.
Provides controllable enhancement based on user preferences.
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
