ResVMUNetX: A Low-Light Enhancement Network Based on VMamba
<|reference_start|>ResVMUNetX: A Low-Light Enhancement Network Based on VMamba: This study presents ResVMUNetX, a novel image enhancement network for low-light conditions, addressing the limitations of existing deep learning methods in capturing long-range image information. Leveraging error regression and an efficient VMamba architecture, ResVMUNetX enhances brightness, recovers structural details, and removes noise through a two-step process involving direct pixel addition and a specialized Denoise CNN module. Demonstrating superior performance on the LOL dataset, ResVMUNetX significantly improves image clarity and quality with reduced computational demands, achieving real-time processing speeds of up to 70 frames per second. This confirms its effectiveness in enhancing low-light images and its potential for practical, real-time applications.<|reference_end|>
@article{wang2024dpec:,
    title={DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image
  Clarity},
    author={Shuang Wang, Qianwen Lu, Boxing Peng, Yihe Nie and Qingchuan Tao},
    journal={arXiv preprint arXiv:2407.09553},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09553},
    primaryClass={cs.CV cs.AI}
}