Abstract
Low-light image enhancement aims to improve the visibility of degraded images to better align with human visual perception. While diffusion-based methods have shown promising performance due to their strong generative capabilities. However, their unidirectional modelling of degradation often struggles to capture the complexity of real-world degradation patterns, leading to structural inconsistencies and pixel misalignments. To address these challenges, we propose a bidirectional diffusion optimization mechanism that jointly models the degradation processes of both low-light and normal-light images, enabling more precise degradation parameter matching and enhancing generation quality. Specifically, we perform bidirectional diffusion-from low-to-normal light and from normal-to-low light during training and introduce an adaptive feature interaction block (AFI) to refine feature representation. By leveraging the complementarity between these two paths, our approach imposes an implicit symmetry constraint on illumination attenuation and noise distribution, facilitating consistent degradation learning and improving the models ability to perceive illumination and detail degradation. Additionally, we design a reflection-aware correction module (RACM) to guide color restoration post-denoising and suppress overexposed regions, ensuring content consistency and generating high-quality images that align with human visual perception. Extensive experiments on multiple benchmark datasets demonstrate that our method outperforms state-of-the-art methods in both quantitative and qualitative evaluations while generalizing effectively to diverse degradation scenarios. Code at this https URL
Abstract (translated)
低光图像增强旨在改善降质图像的可视性，以更好地与人类视觉感知相匹配。虽然基于扩散的方法由于其强大的生成能力显示出有前景的表现，但它们对退化过程的一维建模往往难以捕捉现实世界中复杂多变的退化模式，导致结构上的不一致和像素错位问题。为了解决这些挑战，我们提出了一种双向扩散优化机制，该机制同时建模低光图像和正常光照图像的退化过程，从而实现更精确的退化参数匹配并提升生成质量。具体而言，在训练过程中，我们进行从低光到正常光照以及从正常光照回到低光的双向扩散，并引入自适应特征交互块（AFI）来优化特征表示。通过利用这两条路径之间的互补性，我们的方法在照明衰减和噪声分布上施加了隐式的对称约束，促进了退化学习的一致性和提高了模型感知照明及细节损失的能力。 此外，我们设计了一个反射感知校正模块（RACM），用于指导去噪后的颜色恢复，并抑制过曝区域的产生，确保内容一致性并生成高质量图像以匹配人类视觉感知。在多个基准数据集上的广泛实验表明，我们的方法无论是在定量还是定性评估中均超越了现有最佳方法，并且能够有效地泛化到各种退化场景。 代码可在该链接处访问：[此 URL]
URL
https://arxiv.org/abs/2507.18144
