HSR-KAN: Efficient Hyperspectral Image Super-Resolution via Kolmogorov-Arnold Networks

TL;DR

This paper introduces HSR-KAN, an efficient hyperspectral image super-resolution model that fuses low-resolution hyperspectral images with high-resolution multispectral images using Kolmogorov-Arnold Networks and attention mechanisms, achieving superior results.

Contribution

The paper proposes a novel HSI super-resolution framework based on Kolmogorov-Arnold Networks with a specialized fusion module and spectral attention, improving detail recovery and avoiding the Curse of Dimensionality.

Findings

HSR-KAN outperforms state-of-the-art methods in qualitative assessments.

The model effectively preserves spectral and spatial details.

Extensive experiments validate the superiority of the proposed approach.

Abstract

Hyperspectral images (HSIs) have great potential in various visual tasks due to their rich spectral information. However, obtaining high-resolution hyperspectral images remains challenging due to limitations of physical imaging. Inspired by Kolmogorov-Arnold Networks (KANs), we propose an efficient HSI super-resolution (HSI-SR) model to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). To achieve the effective integration of spatial information from HR-MSI, we design a fusion module based on KANs, called KAN-Fusion. Further inspired by the channel attention mechanism, we design a spectral channel attention module called KAN Channel Attention Block (KAN-CAB) for post-fusion feature extraction. As a channel attention module integrated with KANs, KAN-CAB not only enhances the fine-grained adjustment ability of deep networks, enabling networks to accurately simulate details of spectral sequences and spatial textures, but also effectively avoid Curse of Dimensionality. Extensive experiments show that, compared to current state-of-the-art HSI-SR methods, proposed HSR-KAN achieves the best performance in terms of both qualitative and quantitative assessments. Our code is available at: https://github.com/Baisonm-Li/HSR-KAN.