Recently, implicit neural representations (INR) have made significant strides
in various vision-related domains, providing a novel solution for Multispectral
and Hyperspectral Image Fusion (MHIF) tasks. However, INR is prone to losing
high-frequency information and is confined to the lack of global perceptual
capabilities. To address these issues, this paper introduces a Fourier-enhanced
Implicit Neural Fusion Network (FeINFN) specifically designed for MHIF task,
targeting the following phenomena: The Fourier amplitudes of the HR-HSI latent
code and LR-HSI are remarkably similar; however, their phases exhibit different
patterns. In FeINFN, we innovatively propose a spatial and frequency implicit
fusion function (Spa-Fre IFF), helping INR capture high-frequency information
and expanding the receptive field. Besides, a new decoder employing a complex
Gabor wavelet activation function, called Spatial-Frequency Interactive Decoder
(SFID), is invented to enhance the interaction of INR features. Especially, we
further theoretically prove that the Gabor wavelet activation possesses a
time-frequency tightness property that favors learning the optimal bandwidths
in the decoder. Experiments on two benchmark MHIF datasets verify the
state-of-the-art (SOTA) performance of the proposed method, both visually and
quantitatively. Also, ablation studies demonstrate the mentioned contributions.
The code will be available on Anonymous GitHub
(this https URL) after possible acceptance.

该论文介绍了一种特定设计用于多光谱和高光谱图像融合任务的傅里叶增强的隐式神经融合网络，通过创新地提出了一种空间和频率隐式融合函数（Spa-Fre IFF）来捕捉高频信息并扩展感受野，并发明了一种新的解码器，使用复数 Gabor 小波激活函数，称为空间频率交互解码器（SFID），以增强隐式神经表示特征的交互作用。在两个基准多光谱和高光谱图像融合数据集上的实验证明了该方法在视觉上和定量上的最先进表现，同时消融研究证明了所提出的贡献。