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Multispectral and Hyperspectral Image Fusion (MHIF) is a practical task thataims to fuse a high-resolution multispectral image (HR-MSI) and alow-resolution hyperspectral image (LR-HSI) of the same scene to obtain ahigh-resolution hyperspectral image (HR-HSI). Benefiting from powerfulinductive bias capability, CNN-based methods have achieved great success in theMHIF task. However, they lack certain interpretability and require convolutionstructures be stacked to enhance performance. Recently, Implicit NeuralRepresentation (INR) has achieved good performance and interpretability in 2Dtasks due to its ability to locally interpolate samples and utilize multimodalcontent such as pixels and coordinates. Although INR-based approaches showpromise, they require extra construction of high-frequency information(\emph{e.g.,} positional encoding). In this paper, inspired by previous work ofMHIF task, we realize that HR-MSI could serve as a high-frequency detailauxiliary input, leading us to propose a novel INR-based hyperspectral fusionfunction named Implicit Neural Feature Fusion Function (INF). As an elaboratestructure, it solves the MHIF task and addresses deficiencies in the INR-basedapproaches. 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 INFincorporates 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) thatachieves 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.

Implicit Neural Feature Fusion Function for Multispectral and Hyperspectral Image Fusion