KAN See In the Dark

Aoxiang Ning; Minglong Xue; Jinhong He; Chengyun Song

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arXiv · 2409.03404

KAN See In the Dark

Aoxiang Ning·Minglong Xue·Jinhong He·Chengyun Song

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Abstract

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.

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Aoxiang Ning, Minglong Xue, Jinhong He, Chengyun Song. 2024-09-05. KAN See In the Dark. https://arxiv.org/abs/2409.03404

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