SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2507.15520v1
• Date: Mon, 21 Jul 2025 11:38:56 GMT
• Title: SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement
• Authors: Hanting Li, Fei Zhou, Xin Sun, Yang Hua, Jungong Han, Liang-Jie Zhang,
• Abstract summary: Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination.<n>Recent Transformer-based low-light enhancement methods have made promising progress in recovering global illumination.<n>We present a Spatially-Adaptive Illumination-Guided Transformer framework that enables accurate illumination restoration.
• Score: 58.79901582809091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/LHTcode/SAIGFormer.git.

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