SAIGFormer: A Spatially-Adaptive Illumination-Guided Network for Low-Light Image Enhancement
This work addresses a specific challenge in low-light image enhancement for computer vision applications, representing an incremental improvement over existing Transformer-based methods.
The paper tackled the problem of non-uniform lighting in low-light image enhancement, such as backlit and shadow scenarios, by proposing SAIGFormer, which achieved superior performance on five standard datasets and a cross-domain benchmark, outperforming state-of-the-art methods in quantitative and qualitative metrics.
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.