Abstract
In low-light image enhancement, Retinex-based deep learning methods have garnered significant attention due to their exceptional interpretability. These methods decompose images into mutually independent illumination and reflectance components, allows each component to be enhanced separately. In fact, achieving perfect decomposition of illumination and reflectance components proves to be quite challenging, with some residuals still existing after decomposition. In this paper, we formally name these residuals as inter-component residuals (ICR), which has been largely underestimated by previous methods. In our investigation, ICR not only affects the accuracy of the decomposition but also causes enhanced components to deviate from the ideal outcome, ultimately reducing the final synthesized image quality. To address this issue, we propose a novel Inter-correction Retinex model (IRetinex) to alleviate ICR during the decomposition and enhancement stage. In the decomposition stage, we leverage inter-component residual reduction module to reduce the feature similarity between illumination and reflectance components. In the enhancement stage, we utilize the feature similarity between the two components to detect and mitigate the impact of ICR within each enhancement unit. Extensive experiments on three low-light benchmark datasets demonstrated that by reducing ICR, our method outperforms state-of-the-art approaches both qualitatively and quantitatively.
Abstract (translated)
在低光图像增强领域，基于Retinex的深度学习方法因其出色的可解释性而受到了广泛关注。这些方法将图像分解为相互独立的照明和反射分量，允许每个分量分别进行增强处理。然而，在实践中，实现照明和反射分量的理想分割证明是非常具有挑战性的，即使经过了分解操作之后仍会存在一些残差。在本文中，我们将这些残差正式命名为跨成分残差（ICR），而这一问题在过去的方法中并未得到充分重视。我们的研究发现，ICR不仅影响了分解除的准确性，还导致增强后的分量偏离理想效果，最终降低合成图像的质量。 为了解决这个问题，我们提出了一种新的互矫正Retinex模型（IRetinex）来减轻在分解和增强阶段中的跨成分残差问题。在分解阶段，我们利用跨成分残差减少模块来降低照明与反射分量之间的特征相似性；而在增强阶段，我们则通过分析两个分量间的特征相似性，在每个增强单元中检测并缓解ICR的影响。 我们在三个低光基准数据集上的大量实验表明，我们的方法通过减少ICR不仅在定性和定量评估上都超越了当前最先进的技术。
URL
https://arxiv.org/abs/2508.09009
