用KAN网络提升暗光图像增强效果，更准更可解释。
KAN See In the Dark
- 引入基于样条的KAN模块，捕捉光照非线性关系
- 在多个数据集上超越现有方法，视觉效果更优
- 适合需要高精度和可解释性的低级视觉任务
现有暗光图像增强方法因光照不均和噪声影响，难以拟合正常图像与暗光图像间的复杂非线性关系。最近提出的科尔莫戈罗夫-阿诺德网络（KAN）具有基于样条的卷积层和可学习激活函数，能有效捕捉非线性依赖。本文设计了基于KAN的KAN-Block，并首次将其应用于暗光图像增强。该方法克服了传统线性网络结构限制及可解释性不足的问题，进一步展示了KAN在低层视觉任务中的潜力。针对当前方法感知能力差以及逆扩散过程的随机性，我们进一步引入频域感知机制以实现视觉导向增强。大量实验表明，该方法在多个基准数据集上表现优异。代码将开源：https://github.com/AXNing/KSID
原文摘要 · Abstract (English)
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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