Hyperspectral images (HSIs) provide rich spectral information, making them valuable in various visual tasks. However, obtaining high-resolution HSIs is a challenge due to limitations in physical imaging. This article introduces a novel HSI super-resolution (HSI-SR) model that addresses this challenge by fusing a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution HSI (HR-HSI).
KAN-Fusion: Enhancing Spatial Information Integration
The key aspect of the proposed HSI-SR model is the fusion module called KAN-Fusion, inspired by Kolmogorov-Arnold Networks (KANs). KANs are known for their effectiveness in incorporating spatial information. By leveraging KANs, the fusion module allows for the efficient integration of spatial information from the HR-MSI.
KAN-CAB: Spectral Channel Attention for Feature Extraction
Another essential component of the HSI-SR model is the KAN Channel Attention Block (KAN-CAB), which incorporates a spectral channel attention mechanism. This module enhances the fine-grained adjustment ability of deep networks, enabling them to accurately capture the details of spectral sequences and spatial textures.
Overcoming Curse of Dimensionality
One advantage of the KAN-CAB module is its ability to effectively address the Curse of Dimensionality (COD) in hyperspectral data. COD refers to the challenge of dealing with high-dimensional data, which can negatively impact the performance of deep networks. By integrating channel attention with KANs, KAN-CAB mitigates COD, enabling improved performance in HSI-SR tasks.
Superior Performance
The proposed HSR-KAN model outperforms current state-of-the-art HSI-SR methods in both qualitative and quantitative assessments. Extensive experiments validate its superior performance and demonstrate its ability to generate high-resolution HSIs with enhanced details.
Overall, the combination of KAN-Fusion for spatial information integration and KAN-CAB for spectral channel attention makes the HSI-SR model a promising approach for enhancing the resolution of hyperspectral images. Further research and exploration of this model may lead to advancements in various applications that rely on high-resolution HSIs.