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 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.
Abstract (translated)
低光图像增强（LLIE）旨在改善低光图像。然而，现有的方法面临两个挑战：（1）从不同亮度退化中恢复修复的不确定性；（2）由于噪声抑制和光增强而丢失纹理和颜色信息。在本文中，我们提出了一种新增强方法，称为CodeEnhance，通过利用量化先验信息和图像修复来解决这些挑战。特别地，我们将LLIE重新表述为从低光图像中学习图像到编码映射，这是从高质量图像中学习的高质量图像。为了增强这个过程，我们引入了一个语义嵌入模块（SEM），以将语义信息与低级特征集成，并设计了一个Codebook Shift（CS）机制，旨在将预先学习的编码器适应该低光数据集的显著特征。此外，我们还介绍了交互式特征转换（IFT）模块，用于在图像重建过程中修复纹理和颜色信息，并允许根据用户偏好进行交互式增强。在现实世界和合成基准上进行的大量实验证明，引入先验知识和可控制信息传递 significantly增强了LLIE在质量和保真度方面的性能。所提出的CodeEnhance在各种退化中表现出卓越的鲁棒性，包括不均匀光照、噪声和颜色失真。
URL
https://arxiv.org/abs/2404.05253
