Multispectral and Hyperspectral Image Fusion (MHIF) is a practical task that aims to fuse a high-resolution multispectral image (HR-MSI) and a low-resolution hyperspectral image (LR-HSI) of the same scene to obtain a high-resolution hyperspectral image (HR-HSI). Benefiting from powerful inductive bias capability, CNN-based methods have achieved great success in the MHIF task. However, they lack certain interpretability and require convolution structures be stacked to enhance performance. Recently, Implicit Neural Representation (INR) has achieved good performance and interpretability in 2D tasks due to its ability to locally interpolate samples and utilize multimodal content such as pixels and coordinates. Although INR-based approaches show promise, they require extra construction of high-frequency information (\emph{e.g.,} positional encoding). In this paper, inspired by previous work of MHIF task, we realize that HR-MSI could serve as a high-frequency detail auxiliary input, leading us to propose a novel INR-based hyperspectral fusion function named Implicit Neural Feature Fusion Function (INF). As an elaborate structure, it solves the MHIF task and addresses deficiencies in the INR-based approaches. Specifically, our INF designs a Dual High-Frequency Fusion (DHFF) structure that obtains high-frequency information twice from HR-MSI and LR-HSI, then subtly fuses them with coordinate information. Moreover, the proposed INF incorporates a parameter-free method named INR with cosine similarity (INR-CS) that uses cosine similarity to generate local weights through feature vectors. Based on INF, we construct an Implicit Neural Fusion Network (INFN) that achieves state-of-the-art performance for MHIF tasks of two public datasets, \emph{i.e.,} CAVE and Harvard. The code will soon be made available on GitHub.

翻译：多光谱与高光谱图像融合（MHIF）是一项实用任务，旨在融合同一场景的高分辨率多光谱图像（HR-MSI）和低分辨率高光谱图像（LR-HSI），以获得高分辨率高光谱图像（HR-HSI）。得益于强大的归纳偏置能力，基于CNN的方法在MHIF任务中取得了巨大成功。然而，这些方法缺乏一定可解释性，且需要通过堆叠卷积结构来增强性能。近年来，隐含神经表示（INR）因其能够局部插值样本并利用像素与坐标等多模态内容，在二维任务中展现出良好性能与可解释性。尽管基于INR的方法前景可期，但它们需要额外构建高频信息（例如位置编码）。本文受MHIF任务先前工作的启发，意识到HR-MSI可作为高频细节辅助输入，进而提出一种新型基于INR的高光谱融合函数——隐含神经特征融合函数（INF）。作为精细设计的结构，INF解决了MHIF任务，并弥补了基于INR方法的不足。具体而言，我们的INF设计了双高频融合（DHFF）结构，从HR-MSI和LR-HSI中两次提取高频信息，随后巧妙地将它们与坐标信息融合。此外，所提出的INF还引入了一种名为余弦相似度隐含神经表示（INR-CS）的无参数方法，利用余弦相似度通过特征向量生成局部权重。基于INF，我们构建了隐含神经融合网络（INFN），在两个公开数据集（即CAVE和Harvard）的MHIF任务上均实现了最先进的性能。代码将在GitHub上开源。