MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model

• URL: http://arxiv.org/abs/2607.03013v2
• Date: Thu, 09 Jul 2026 02:26:14 GMT
• Title: MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model
• Abstract summary: MambaLIE is a Scene Light Intensity-Boosted Low-Light Image Enhancement method based on a State Space Model (SSM)<n>To better model the illumination while maintaining computational efficiency, we propose the Locally Enhanced State Space Model (LESSM)<n>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.
• Score: 21.71348874208162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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