CoFusion: Multispectral and Hyperspectral Image Fusion via Spectral Coordinate Attention

TL;DR

CoFusion is a novel framework for multispectral and hyperspectral image fusion that models cross-scale and cross-modal dependencies to improve spatial detail and spectral fidelity.

Contribution

It introduces a unified spatial-spectral collaborative fusion framework with multi-scale architecture and specialized modules for enhanced image reconstruction.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Achieves better spatial detail and spectral fidelity.

Demonstrates robustness across multiple datasets.

Abstract

Multispectral and Hyperspectral Image Fusion (MHIF) aims to reconstruct high-resolution images by integrating low-resolution hyperspectral images (LRHSI) and high-resolution multispectral images (HRMSI). However, existing methods face limitations in modeling cross-scale interactions and spatial-spectral collaboration, making it difficult to achieve an optimal trade-off between spatial detail enhancement and spectral fidelity. To address this challenge, we propose CoFusion: a unified spatial-spectral collaborative fusion framework that explicitly models cross-scale and cross-modal dependencies. Specifically, a Multi-Scale Generator (MSG) is designed to construct a three-level pyramidal architecture, enabling the effective integration of global semantics and local details. Within each scale, a dual-branch strategy is employed: the Spatial Coordinate-Aware Mixing module (SpaCAM) is utilized to capture multi-scale spatial contexts, while the Spectral Coordinate-Aware Mixing module (SpeCAM) enhances spectral representations through frequency decomposition and coordinate mixing. Furthermore, we introduce the Spatial-Spectral Cross-Fusion Module (SSCFM) to perform dynamic cross-modal alignment and complementary feature fusion. Extensive experiments on multiple benchmark datasets demonstrate that CoFusion consistently outperforms state-of-the-art methods, achieving superior performance in both spatial reconstruction and spectral consistency.