MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model
2026-07-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors developed a new method called MambaLIE to improve pictures taken in low light, which are often dark and unclear. They combine information about the brightness of the scene with the original dark image to better brighten it. Their method uses a special model that efficiently captures both big-picture and detailed parts of the image without needing too much computing power. Tests show their approach works better and faster than existing methods, making it useful for devices like smartphones with limited resources.
Low-Light Image EnhancementConvolutional Neural Networks (CNNs)TransformersState Space ModelScene Light IntensityLocally Enhanced State Space Model (LESSM)Long-Range DependenciesComputational EfficiencyImage ProcessingHierarchical Features
Authors
Wanshu Fan, Xiangyu Li, Cong Wang, Kin-man Lam, Xin Yang, Haiyan Zhang, Dongsheng Zhou
Abstract
Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks. Existing methods based on Convolutional Neural Networks (CNNs) and Transformers have dominated current low-light image enhancement (LIE) due to their excellent ability to model hierarchical features. However, CNNs operate in local receptive fields that cannot model long-range dependencies, while Transformers overcome this problem but incur substantial computational costs. To address these challenges, we propose MambaLIE, a Scene Light Intensity-Boosted Low-Light Image Enhancement method based on a State Space Model (SSM). We first introduce scene light intensity to improve the structural distribution of illumination, which is then gated with the low-light input to guide enhancement. To better model the illumination while maintaining computational efficiency, we propose the Locally Enhanced State Space Model (LESSM) for efficient light enhancement. Our LESSM contains two branches: an SSM branch and a Local Enhanced branch, where the former is used to model the long-range dependencies with linear time complexity, while the latter is used to enhance local feature representations. Extensive experiments demonstrate that MambaLIE outperforms state-of-the-art CNN-based and Transformer-based LIE methods on four widely used synthetic benchmarks and five publicly available real-world benchmarks in terms of accuracy, speed, and model size, making it suitable for practical deployment on resource-constrained devices.