KAN See In the Dark
<|reference_start|>KAN See In the Dark: Existing low-light image enhancement methods are difficult to fit the complex nonlinear relationship between normal and low-light images due to uneven illumination and noise effects. The recently proposed Kolmogorov-Arnold networks (KANs) feature spline-based convolutional layers and learnable activation functions, which can effectively capture nonlinear dependencies. In this paper, we design a KAN-Block based on KANs and innovatively apply it to low-light image enhancement. This method effectively alleviates the limitations of current methods constrained by linear network structures and lack of interpretability, further demonstrating the potential of KANs in low-level vision tasks. Given the poor perception of current low-light image enhancement methods and the stochastic nature of the inverse diffusion process, we further introduce frequency-domain perception for visually oriented enhancement. Extensive experiments demonstrate the competitive performance of our method on benchmark datasets. The code will be available at: https://github.com/AXNing/KSID}{https://github.com/AXNing/KSID.<|reference_end|>
@article{ning2024kan,
    title={KAN See In the Dark},
    author={Aoxiang Ning, Minglong Xue, Jinhong He and Chengyun Song},
    journal={arXiv preprint arXiv:2409.03404},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03404},
    primaryClass={cs.CV cs.AI}
}