HyFusion：用于高光谱图像融合的增强感受野Transformer
HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image Fusion
AI总结：
针对现有高光谱图像融合方法感受野受限、特征利用不足的问题，提出融合增强感受野模块与特征图复用的双耦合网络HyFusion，在保持轻量高效的同时取得最优融合性能，树立了新基准。
摘要：
高光谱图像（HSI）融合旨在解决从高分辨率多光谱图像（HR-MSI）和低分辨率高光谱图像（LR-HSI）重建高分辨率高光谱图像（HR-HSI）的难题，鉴于获取高质量HSI的高成本和硬件限制，这是一项至关重要的任务。现有方法虽然利用了空间和光谱关系，但往往存在感受野受限和特征利用不足的问题，导致性能欠佳。此外，高质量HSI数据的稀缺性凸显了高效利用数据以最大化重建质量的重要性。为解决这些问题，我们提出HyFusion，这是一种新型的双耦合网络（Dual-Coupled Network, DCN）框架，旨在增强跨域特征提取并实现有效的特征图复用。该框架首先通过专用子网络处理HR-MSI和LR-HSI输入，这些子网络在特征提取过程中相互增强，保留互补的空间和光谱细节。HyFusion的核心是增强感受野模块（Enhanced Reception Field Block, ERFB），它结合了滑动窗口注意力和密集连接来扩大感受野，在最小化信息损失的同时有效捕获长距离依赖关系。大量实验表明，HyFusion在HR-MSI/LR-HSI融合任务中取得了最优（state-of-the-art）性能，在保持紧凑模型尺寸和计算效率的同时，显著提升了重建质量。通过将增强感受野和特征图复用整合到耦合网络架构中，HyFusion为资源受限场景下的HSI融合提供了实用且有效的解决方案，树立了高光谱成像领域的新基准。我们的代码将公开提供。
英文摘要：
Hyperspectral image (HSI) fusion addresses the challenge of reconstructing High-Resolution HSIs (HR-HSIs) from High-Resolution Multispectral images (HR-MSIs) and Low-Resolution HSIs (LR-HSIs), a critical task given the high costs and hardware limitations associated with acquiring high-quality HSIs. While existing methods leverage spatial and spectral relationships, they often suffer from limited receptive fields and insufficient feature utilization, leading to suboptimal performance. Furthermore, the scarcity of high-quality HSI data highlights the importance of efficient data utilization to maximize reconstruction quality. To address these issues, we propose HyFusion, a novel Dual-Coupled Network (DCN) framework designed to enhance cross-domain feature extraction and enable effective feature map reusing. The framework first processes HR-MSI and LR-HSI inputs through specialized subnetworks that mutually enhance each other during feature extraction, preserving complementary spatial and spectral details. At its core, HyFusion utilizes an Enhanced Reception Field Block (ERFB), which combines shifting-window attention and dense connections to expand the receptive field, effectively capturing long-range dependencies while minimizing information loss. Extensive experiments demonstrate that HyFusion achieves state-of-the-art performance in HR-MSI/LR-HSI fusion, significantly improving reconstruction quality while maintaining a compact model size and computational efficiency. By integrating enhanced receptive fields and feature map reusing into a coupled network architecture, HyFusion provides a practical and effective solution for HSI fusion in resource-constrained scenarios, setting a new benchmark in hyperspectral imaging. Our code will be publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
