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vision tasks.\n  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.\n  However, CNNs operate in local receptive fields that cannot model long-range dependencies, while Transformers overcome this problem but incur substantial computational costs.\n  To address these challenges, we propose MambaLIE, a Scene Light Intensity-Boosted Low-Light Image Enhancement method based on a State Space Model (SSM).\n  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.\n  ","title":"MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model","url":"https://arxiv.org/abs/2607.03013","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.03013v1 Announce Type: cross \nAbstract: Images captured by consumer electronic devices, 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