标题： 高光谱图像超分辨率的高效反馈门网络 显示英文标题

标题： Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution

摘要： 即使没有辅助图像，也可以设计单高光谱图像超分辨率（SHSR）方法来提高高光谱图像的空间分辨率。然而，未能充分探索波段间的相干性以及空谱信息，导致了SHSR性能有限。在本研究中，我们提出了一种新颖的基于组的SHSR方法，称为高效反馈门网络，该方法使用涉及大内核卷积和光谱交互的各种反馈和门操作。特别是，通过为相邻组提供不同的指导，我们可以使用通道混洗和混洗和渐进式扩张融合模块（SPDFM）中的扩张卷积来学习丰富的波段信息和分层高光谱空间信息。此外，我们开发了一个宽边界感知门块和一个光谱增强门块来构建空谱强化门模块（SSRGM），并高效地获得高度代表性的空谱特征。此外，我们将三维SSRGM应用于增强高光谱数据的整体信息和一致性。在三个高光谱数据集上的实验结果表明，所提出的网络在光谱保真度和空间内容重建方面优于最先进的方法。 显示英文摘要

摘要： Even without auxiliary images, single hyperspectral image super-resolution (SHSR) methods can be designed to improve the spatial resolution of hyperspectral images. However, failing to explore coherence thoroughly along bands and spatial-spectral information leads to the limited performance of the SHSR. In this study, we propose a novel group-based SHSR method termed the efficient feedback gate network, which uses various feedbacks and gate operations involving large kernel convolutions and spectral interactions. In particular, by providing different guidance for neighboring groups, we can learn rich band information and hierarchical hyperspectral spatial information using channel shuffling and dilatation convolution in shuffled and progressive dilated fusion module(SPDFM). Moreover, we develop a wide-bound perception gate block and a spectrum enhancement gate block to construct the spatial-spectral reinforcement gate module (SSRGM) and obtain highly representative spatial-spectral features efficiently. Additionally, we apply a three-dimensional SSRGM to enhance holistic information and coherence for hyperspectral data. The experimental results on three hyperspectral datasets demonstrate the superior performance of the proposed network over the state-of-the-art methods in terms of spectral fidelity and spatial content reconstruction.