  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.
