ResVMUNetX: A Low-Light Enhancement Network Based on VMamba
- URL: http://arxiv.org/abs/2407.09553v2
- Date: Sun, 21 Jul 2024 06:43:27 GMT
- Title: ResVMUNetX: A Low-Light Enhancement Network Based on VMamba
- Authors: Shuang Wang, Qingchuan Tao, Zhenming Tang,
- Abstract summary: ResVMUNetX enhances brightness, recovers structural details, and removes noise in low-light images.
It achieves 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.
- Score: 3.1121020391193777
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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