MambaLIE: State Space Model enhances low-light images efficiently

Researchers have developed MambaLIE, a novel method for low-light image enhancement that utilizes a State Space Model (SSM). This approach aims to overcome the limitations of existing Convolutional Neural Networks (CNNs) and Transformers, which struggle with either long-range dependencies or computational costs. MambaLIE introduces scene light intensity to guide the enhancement process and proposes a Locally Enhanced State Space Model (LESSM) that combines SSM for long-range modeling with a local enhancement branch for efficiency. Experiments show MambaLIE surpasses current methods in accuracy, speed, and model size, making it suitable for devices with limited resources. AI

IMPACT This new method could improve image quality in low-light conditions, benefiting applications from consumer devices to specialized vision tasks.

RANK_REASON This is a research paper detailing a new method for image enhancement. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• convolutional neural network
• Lessmann
• Locally Enhanced State Space Model
• MambaLIE
• State Space Model
• transformers

• paper
• model release

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