arXiv:2607.03013v1 Announce Type: cross 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 Transf
The paper leverages recent advancements in State Space Models like Mamba to address known limitations of CNNs and Transformers in low-light image enhancement, indicating a current trend in AI research to explore new architectural paradigms.
Improving low-light image enhancement has broad applications across consumer electronics, security, autonomous systems, and medical imaging, enhancing the utility and reliability of machine vision in challenging conditions.
This research introduces a State Space Model (Mamba) into a crucial computer vision task where CNNs and Transformers have traditionally dominated, suggesting a potential shift in preferred architectures for certain AI applications.
- · Consumers of electronic devices
- · Security and surveillance sectors
- · Autonomous vehicle developers
- · AI model developers
- · Legacy image processing techniques
- · AI models heavily reliant on clear visual input
Wider adoption and improved performance of image enhancement features in next-generation devices and software.
Reduced dependence on specialized hardware or lighting conditions for certain visual tasks, broadening the scope of AI applications.
Enhanced data quality from existing sensor infrastructure, potentially accelerating developments in fields like remote sensing or scientific imaging.
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Read at arXiv cs.AI