Hyperspectral imaging has become an increasingly used tool in the analysis of works of art. However, the quality of the acquired data and the processing of that data to produce accurate and reproducible spectral image cubes can be a challenge to many cultural heritage users. The calibration of data that is both spectrally and spatially accurate is an essential step in order to obtain useful and relevant results from hyperspectral imaging. Data that is too noisy or inaccurate will produce sub-optimal results when used for pigment mapping, the detection of hidden features, change detection or for quantitative spectral documentation. To help address this, therefore, we will examine the specific acquisition and calibration workflows necessary for works of art. These workflows includes the key parameters that must be addressed during acquisition and the essential steps and issues at each of the stages required during post-processing in order to fully calibrate hyperspectral data. In addition we will look in detail at the key issues that affect data quality and propose practical solutions that can make significant differences to overall hyperspectral image quality.
LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition
College of Information Science & Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
Deep intra-operative illumination calibration of hyperspectral cameras
Siemens AG（西门子股份公司）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）；University Medical Center Mannheim（曼海姆大学医学中心）；DKFZ Hector Cancer Institute at the University Medical Center Mannheim（德国癌症研究中心赫克曼海姆大学医学中心癌症研究所）
mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics
Weldon School of Biomedical Engineering, Purdue University（普渡大学生物医学工程学院）；Regenstrief Center for Healthcare Engineering（里真斯特里夫医疗保健工程中心）；Purdue Quantum Science and Engineering Institute（普渡大学量子科学与工程研究所）
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）
Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；International Research Center of Big Data for Sustainable Development Goals（可持续发展大数据国际研究中心）
In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection
University of Las Palmas de Gran Canaria（大加那利岛拉斯帕尔马斯大学）；University Hospital Doctor Negrin（内格林大学医院）；University Hospital Southampton（南安普顿大学医院）；Addenbrookes Hospital（阿登布鲁克医院）；University of Cambridge（剑桥大学）；AKKA Technologies（AKKA Technologies公司）；Imperial College London（伦敦帝国理工学院）；Universidad Politécnica de Madrid（马德里理工大学）；Ecole Nationale Supérieure des Mines de Paris（巴黎高等矿业学校）；MINES ParisTech（巴黎矿业学院）
Compressive spectral image classification using 3D coded convolutional neural network
Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China, School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院光电成像技术与系统教育部重点实验室）；Department of Electrical and Computer Engineering, University of Delaware（特拉华大学电气与计算机工程系）
Accelerating 4D Hyperspectral Imaging through Physics-Informed Neural Representation and Adaptive Sampling
Dept. of Electrical and Computer Engineering, UC San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Dept. of Chemistry, UC San Diego（加州大学圣地亚哥分校化学系）；Materials Science and Engineering Program, UC San Diego（加州大学圣地亚哥分校材料科学与工程项目）
A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging
School of Biomedical Engineering & Imaging Sciences King’s College London London, UK（生物医学工程与成像科学学院 英国伦敦国王学院）；Department of Neurosurgery King’s College Hospital NHS Foundation Trust, London, UK（神经外科部门 英国伦敦国王学院医院 NHS基金会信托）
A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection
Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science（罗切斯特理工学院，切斯特·F·卡尔森成像科学中心）；Lamont Doherty Earth Observatory, Columbia University（拉蒙特-多赫蒂地球观测站，哥伦比亚大学）；Demining Research Community（排雷研究社区）
HSIGene: A Foundation Model For Hyperspectral Image Generation
School of Mathematics and Statistics, Northwestern Polytechnical University（西北工业大学数学与统计学院）；Xidian University（西安电子科技大学）；Pazhou Laboratory (Huangpu)（琶洲实验室（黄埔））
CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image Fusion
Miin Wu School of Computing, National Cheng Kung University（成功大学米恩·吴计算机学院）；Department of Electrical Engineering, National Taiwan Normal University（台湾师范大学电机工程学系）
Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging
Carl Zeiss AG（卡尔蔡司股份公司）；University of Padua（帕多瓦大学）；University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；Carl Zeiss Meditec AG（卡尔蔡司医疗科技股份公司）；IMEC（比利时微电子研究中心）；University Hospitals Leuven（鲁汶大学医院）；KU Leuven（鲁汶大学）
