This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution. The dataset was first released and promoted during the PIRM2018 spectral image super-resolution challenge. To the best of our knowledge, the dataset is the first of its kind, comprising 350 registered colour-spectral image pairs. The dataset has been used for the two tracks of the challenge and, for each of these, we have provided a split into training, validation and testing. This arrangement is a result of the challenge structure and phases, with the first track focusing on example-based spectral image super-resolution and the second one aiming at exploiting the registered stereo colour imagery to improve the resolution of the spectral images. Each of the tracks and splits has been selected to be consistent across a number of image quality metrics. The dataset is quite general in nature and can be used for a wide variety of applications in addition to the development of spectral image super-resolution methods.
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。
Phy-CoSF: Physics-Guided Continuous Spectral Fields Reconstruction and Super-Resolution for Snapshot Compressive Imaging
College of Communication Engineering, Jilin University, Changchun 130012, China.；School of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China.；School of Electrical \& Electronic Engineering, Nanyang Technological University, Singapore 639798.；Department of Computer；Information Science, University of Macau, Macau 999078, China.；College of Computer Science；Technology, Jilin University, Changchun 130012, China.；College of Artificial Intelligence, Jilin University, Changchun 130012, China.；School of Information；Communication Engineering, University of Electronic Science
Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance
Beijing Institute of Technology（北京理工大学）；Hangzhou Dianzi University（杭州电子科技大学）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
Model-Guided Network with Cluster-Based Operators for Spatio-Spectral Super-Resolution
Institute of Applied Computing and Community Code (IAC3)（应用计算与社区代码研究所）；Dept. of Mathematics and Computer Science, Universitat de les Illes Balears（数学与计算机科学系，巴利阿里大学）
SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
University of Georgia（佐治亚大学）；Stanford University（斯坦福大学）；Alphabet Inc.（Alphabet公司）；University of British Columbia（不列颠哥伦比亚大学）；University of Connecticut（康涅狄格大学）
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）
Radiative-Structured Neural Operator for Continuous Spectral Super-Resolution
School of Statistics and Data Science, LEBPS, KLMDASR and LPMC（统计与数据科学学院，LEBPS，KLMDASR和LPMC）；Image Processing Center, School of Astronautics（图像处理中心，航天学院）
HSRMamba: Contextual Spatial-Spectral State Space Model for Single Image Hyperspectral Super-Resolution
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程研究中心，计算机学院，武汉大学）；Aerospace Information Research Institute, Henan Academy of Sciences（航天信息研究所，河南科学院）
Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning
Simon Fraser University（西蒙弗雷泽大学）；Wake Forest University School of Medicine（维克森林大学医学院）；University of British Columbia（不列颠哥伦比亚大学）；University College London（伦敦大学学院）
