Current deep learning methods for low-light image enhancement (LLIE) typically rely on pixel-wise mapping learned from paired data. However, these methods often overlook the importance of considering degradation representations, which can lead to sub-optimal outcomes. In this paper, we address this limitation by proposing a degradation-aware learning scheme for LLIE using diffusion models, which effectively integrates degradation and image priors into the diffusion process, resulting in improved image enhancement. Our proposed degradation-aware learning scheme is based on the understanding that degradation representations play a crucial role in accurately modeling and capturing the specific degradation patterns present in low-light images. To this end, First, a joint learning framework for both image generation and image enhancement is presented to learn the degradation representations. Second, to leverage the learned degradation representations, we develop a Low-Light Diffusion model (LLDiffusion) with a well-designed dynamic diffusion module. This module takes into account both the color map and the latent degradation representations to guide the diffusion process. By incorporating these conditioning factors, the proposed LLDiffusion can effectively enhance low-light images, considering both the inherent degradation patterns and the desired color fidelity. Finally, we evaluate our proposed method on several well-known benchmark datasets, including synthetic and real-world unpaired datasets. Extensive experiments on public benchmarks demonstrate that our LLDiffusion outperforms state-of-the-art LLIE methods both quantitatively and qualitatively. The source code and pre-trained models are available at https://github.com/TaoWangzj/LLDiffusion.

翻译：当前用于低光照图像增强（LLIE）的深度学习方法通常依赖于从配对数据中学习像素级映射。然而，这些方法往往忽略了考虑退化表示的重要性，这可能导致次优结果。在本文中，我们通过提出一种基于扩散模型的退化感知学习方案来解决这一局限性，该方案将退化与图像先验有效整合到扩散过程中，从而提升图像增强效果。所提出的退化感知学习方案基于以下理解：退化表示在精确建模和捕捉低光照图像中特定的退化模式方面起着关键作用。为此，首先，我们提出一个联合学习框架，用于同时学习图像生成与图像增强中的退化表示。其次，为利用学习到的退化表示，我们开发了一个低光照扩散模型（LLDiffusion），该模型配备了一个精心设计的动态扩散模块。该模块结合色彩映射和潜在退化表示来引导扩散过程。通过融入这些条件因子，所提出的LLDiffusion能够有效增强低光照图像，同时考虑固有的退化模式和期望的色彩保真度。最后，我们在多个知名基准数据集（包括合成和真实无配对数据集）上评估了所提方法。在公共基准上的大量实验表明，我们的LLDiffusion在定量和定性方面均优于现有最先进的LLIE方法。源代码和预训练模型可从https://github.com/TaoWangzj/LLDiffusion获取。