  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.
