Abstract
Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination. However, they still struggle with non-uniform lighting scenarios, such as backlit and shadow, appearing as over-exposure or inadequate brightness restoration. To address this challenge, we present a Spatially-Adaptive Illumination-Guided Transformer (SAIGFormer) framework that enables accurate illumination restoration. Specifically, we propose a dynamic integral image representation to model the spatially-varying illumination, and further construct a novel Spatially-Adaptive Integral Illumination Estimator ($\text{SAI}^2\text{E}$). Moreover, we introduce an Illumination-Guided Multi-head Self-Attention (IG-MSA) mechanism, which leverages the illumination to calibrate the lightness-relevant features toward visual-pleased illumination enhancement. Extensive experiments on five standard low-light datasets and a cross-domain benchmark (LOL-Blur) demonstrate that our SAIGFormer significantly outperforms state-of-the-art methods in both quantitative and qualitative metrics. In particular, our method achieves superior performance in non-uniform illumination enhancement while exhibiting strong generalization capabilities across multiple datasets. Code is available at this https URL.
Abstract (translated)
最近基于Transformer的低光增强方法在恢复全局照明方面取得了显著进展。然而，它们仍然难以应对非均匀光照场景（如背光和阴影），这些问题会导致过度曝光或亮度恢复不足。为了应对这一挑战，我们提出了一种空间自适应照明引导变换器（SAIGFormer）框架，以实现准确的光照恢复。具体而言，我们提出了动态积分图像表示来建模空间变化的光照，并进一步构建了一个新颖的空间自适应积分光照估计器（$\text{SAI}^2\text{E}$）。此外，我们引入了一种照明引导多头自我注意机制（IG-MSA），该机制利用光照校准与亮度相关的特征以实现视觉愉悦的光照增强。在五个标准低光数据集和一个跨域基准测试（LOL-Blur）上的广泛实验表明，我们的SAIGFormer方法在量化指标和定性评价中都显著优于最先进的方法。特别地，我们提出的方法在非均匀照明增强方面表现出色，并且在多个数据集中展示了强大的泛化能力。代码可在该链接获取。
URL
https://arxiv.org/abs/2507.15520
