用可自适应的激活函数提升多光谱图像融合效果
PAKAN: Pixel Adaptive Kolmogorov-Arnold Network Modules for Pansharpening
- 引入像素自适应的KAN模块，动态调整空间与光谱特征融合方式
- 在多个数据集上实现更优的融合质量，尤其在细节保留上显著提升
- 适合遥感图像处理、高分辨率图像融合方向的研究者参考
全色锐化旨在将全色图像的高分辨率空间信息与多光谱图像的丰富光谱信息融合。现有深度神经网络通常依赖静态激活函数，难以动态建模复杂的非线性映射关系。尽管最近提出的柯尔莫哥洛夫-阿诺德网络（KAN）采用可学习激活函数，但传统KAN在推理阶段缺乏动态适应能力。为此，我们提出像素自适应的柯尔莫哥洛夫-阿诺德网络框架（PAKAN）。基于KAN，设计两种自适应变体：2D自适应KAN在空间维度生成样条求和权重，1D自适应KAN在光谱通道维度生成权重。二者分别用于特征融合（PAKAN 2to1）与特征优化（PAKAN 1to1）。大量实验表明，所提模块显著提升网络性能，验证了像素自适应激活在全色锐化任务中的有效性与优越性。
原文摘要 · Abstract (English)
Pansharpening aims to fuse high-resolution spatial details from panchromatic images with the rich spectral information of multispectral images. Existing deep neural networks for this task typically rely on static activation functions, which limit their ability to dynamically model the complex, non-linear mappings required for optimal spatial-spectral fusion. While the recently introduced Kolmogorov-Arnold Network (KAN) utilizes learnable activation functions, traditional KANs lack dynamic adaptability during inference. To address this limitation, we propose a Pixel Adaptive Kolmogorov-Arnold Network framework. Starting from KAN, we design two adaptive variants: a 2D Adaptive KAN that generates spline summation weights across spatial dimensions and a 1D Adaptive KAN that generates them across spectral channels. These two components are then assembled into PAKAN 2to1 for feature fusion and PAKAN 1to1 for feature refinement. Extensive experiments demonstrate that our proposed modules significantly enhance network performance, proving the effectiveness and superiority of pixel-adaptive activation in pansharpening tasks.
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