Low-light image enhancement is vital for improving the visibility and quality of images captured under suboptimal lighting conditions. Traditional methods often fail to adequately capture local lighting variations and enhance both textural and chromatic details. Recent deep learning-based approaches, while effective, still struggle with generalization across diverse datasets, leading to noise amplification and unnatural color saturation. To address these challenges, the Adaptive Light Enhancement Network (ALEN) is introduced, a novel method that utilizes a classification mechanism to determine whether local or global illumination enhancement is required. ALEN integrates the Swin Light-Classification Transformer (SLCformer) for illuminance categorization, complemented by the Single-Channel Network (SCNet) and Multi-Channel Network (MCNet) for precise illumination and color estimation, respectively. Extensive experiments on publicly available datasets demonstrate ALEN's robust generalization capabilities, outperforming state-of-the-art methods in both quantitative metrics and qualitative assessments. Furthermore, ALEN not only enhances image quality but also improves the performance of high-level vision tasks such as semantic segmentation, showcasing its broader applicability and potential impact. The code for this method and the datasets are available at https://github.com/xingyumex/ALEN}{https://github.com/xingyumex/ALEN

翻译：低光照图像增强对于提升在次优光照条件下捕获图像的可见性与质量至关重要。传统方法往往无法充分捕捉局部光照变化并同时增强纹理与色彩细节。尽管近期基于深度学习的方法取得了成效，但在跨不同数据集的泛化能力上仍存在不足，常导致噪声放大与色彩饱和度失真。为应对这些挑战，本文提出了自适应光照增强网络（ALEN），这是一种新颖的方法，其利用分类机制来判断局部或全局光照增强的需求。ALEN集成了Swin光照分类Transformer（SLCformer）进行照度分类，并辅以单通道网络（SCNet）与多通道网络（MCNet）分别用于精确的光照估计与色彩估计。在公开数据集上的大量实验表明，ALEN具备强大的泛化能力，在定量指标与定性评估上均优于现有先进方法。此外，ALEN不仅提升了图像质量，还改善了如语义分割等高层次视觉任务的性能，展现了其更广泛的适用性与潜在影响。该方法代码及数据集可在 https://github.com/xingyumex/ALEN 获取。