Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks
University of Electronic Science and Technology of China（电子科技大学）；Southwestern University of Finance and Economics（西南财经大学）；University of Salerno（萨勒诺大学）；Institute of Methodologies for Environmental Analysis, CNR-IMAA（意大利国家研究委员会环境分析方法研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）
Hyperspectral images are of crucial importance in order to better understand features of different materials. To reach this goal, they leverage on a high number of spectral bands. However, this interesting characteristic is often paid by a reduced spatial resolution compared with traditional multispectral image systems. In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to an architecture from two folds: one is to utilize the HR-HSI at a different scale to get an output with a satisfied spectral preservation; another one is to apply concepts of multi-resolution analysis to extract high-frequency information, aiming to output high quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves best performance (both qualitatively and quantitatively) compared with recent state-of-the-art hyperspectral image super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as, a better network generalization ability, a limited computational burden, and a robustness with respect to the number of training samples.
Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors
University of Electronic Science and Technology of China（电子科技大学）；Oregon State University（俄勒冈州立大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
Hyperspectral and multispectral image fusion under spectrally varying spatial blurs -- Application to high dimensional infrared astronomical imaging
University of Toulouse（图卢兹大学）；IRIT/INP-ENSEEIHT（图卢兹计算机研究所/国立图卢兹应用科学学院-国立高等电力技术、电子学、计算机、水力学与电信学院）；CNRS（法国国家科学研究中心）；Institut Universitaire de France (IUF)（法国大学研究院）；ISAE-SUPAERO（法国高等航空航天学院）；Institut de Recherche en Astrophysique et Planétologie (IRAP)（天体物理与行星学研究所）
Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Network
Department of Computer Science, Aalto University, Finland（艾尔沃斯大学计算机科学系）；School of Electrical and Electronic Engineering, University of Sheffield, United Kingdom（谢菲尔德大学电子与电气工程学院）
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
1 School of Mathematics；Statistics, Xi'an Jiaotong University 2 School of Mathematics；Information Science, Shaanxi Normal University 3 Shenyang Institute of Automation, Chinese Academy of Sciences
Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院数字地球科学重点实验室）；Flemish Institute for Technological Research (VITO)（弗拉芒技术研究院）；IMEC Research Group, Ghent University（根特大学IMEC研究组）；Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；Technical University of Munich（慕尼黑工业大学）；Univ. Grenoble Alpes, INRIA, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔理工学院、LJK实验室）；Macau University of Science and Technology（澳门科技大学）
Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution
Sanya Oceanographic Institution, Ocean University of China（南海海洋研究所，中国海洋大学）；State Key Laboratory of Physical Oceanography, Ocean University of China（物理海洋学国家重点实验室，中国海洋大学）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电气与计算机工程系）
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution
City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；Huaqiao University（华侨大学）；Xidian University（西安电子科技大学）；University of Macau（澳门大学）
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution
School of Electronic and Information Engineering, Anhui University（安徽大学电子与信息工程学院）；School of Artificial intelligence, Anhui University（安徽大学人工智能学院）；Institutes of Physical Science, Chinese Academy Sciences（中国科学院物理科学研究院）
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
Harbin Institute of Technology（哈尔滨工业大学）；Wuhan University（武汉大学）
高分辨率高光谱人脸图像在弱光环境、欺骗攻击等不可控条件下的人脸相关计算机视觉任务中发挥着重要作用。然而，高光谱人脸图像密集的光谱波段是以有限的光子数量为代价的，平均达到一个狭窄的光谱窗口，这大大降低了高光谱人脸图像的空间分辨率。在本文中，我们研究了如何将深度学习技术应用于高光谱人脸图像超分辨率（HFSR），特别是当训练样本非常有限时。受益于光谱波段的数量，其中每个波段可以被看作是一个图像，我们提出了一个光谱分裂和聚合网络（SSANet）的HFSR有限的训练样本。在浅层，我们将高光谱图像分成不同的光谱组。然后，我们逐渐聚合更深层的相邻波段，以利用光谱相关性。通过这种光谱分裂和聚合策略（SSAS），我们可以将原始高光谱图像分成多个样本（\emph{from less to more}），以支持网络的有效训练，并有效地利用光谱之间的光谱相关性。为了应对小训练样本量（S3）问题的挑战，我们提出了通过自我表征模型和语义诱导增强来扩展训练样本。实验表明，SSANet可以很好地模拟空间和光谱信息的联合相关性。通过扩展训练样本，SSANet可以有效地缓解S3问题。
Rethinking the Upsampling Layer in Hyperspectral Image Super Resolution
Faculty of Data Science, City University of Macau（数据科学学院，澳门城市大学）；College of Oceanography and Space Informatics, China University of Petroleum (East China)（海洋科学与空间信息学院，中国石油大学（华东））；Department of Automation, Tsinghua University（自动化系，清华大学）
Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
Hamburg University of Technology（汉堡工业大学）；University of Bonn（波恩大学）；Klinikum Braunschweig（布伦瑞克医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
