Proc. IEEE Int. Conf. on Acoust, Speech, Signal Process. (ICASSP), to be published. Manuscript submitted October 6th, 2021; revised January 8th, 2022; accepted January 22nd, 2022
To overcome inherent hardware limitations of hyperspectral imaging systems with respect to their spatial resolution, fusion-based hyperspectral image (HSI) super-resolution is attracting increasing attention. This technique aims to fuse a low-resolution (LR) HSI and a conventional high-resolution (HR) RGB image in order to obtain an HR HSI. Recently, deep learning architectures have been used to address the HSI super-resolution problem and have achieved remarkable performance. However, they ignore the degradation model even though this model has a clear physical interpretation and may contribute to improve the performance. We address this problem by proposing a method that, on the one hand, makes use of the linear degradation model in the data-fidelity term of the objective function and, on the other hand, utilizes the output of a convolutional neural network for designing a deep prior regularizer in spectral and spatial gradient domains. Experiments show the performance improvement achieved with this strategy.
Degradation-Aware Metric Prompting for Hyperspectral Image Restoration
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China（北京理工大学计算机科学与技术学院）；School of Computer Science, Wuhan University, Wuhan, Hubei, China（武汉大学计算机学院）；Zhongguancun Academy, Beijing, China（中关村学院）
Physics-Inspired Degradation Models for Hyperspectral Image Fusion
Beijing Institute of Technology（北京理工大学）；Hunan University（湖南大学）；University of Science and Technology of China（中国科学技术大学）；Beijing Normal University（北京师范大学）
PromptHSI: Universal Hyperspectral Image Restoration with Vision-Language Modulated Frequency Adaptation
Institute of Data Science, National Cheng Kung University（成功大学数据科学研究所）；Graduate Institute of Communication Engineering, National Taiwan University（台湾大学通讯工程学系）；NVIDIA（英伟达公司）
Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising
Robotics Institute, Athena Research and Innovation Center（雅典娜研究与创新中心机器人研究所）；School of Electrical and Computer Engineering, National Technical University of Athens（雅典国家技术大学电气与计算机工程学院）
Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology (NJUST)（南京理工大学计算机科学与工程学院）；Geological Exploration Technology Institute of Jiangsu Province（江苏省地质勘查技术院）；Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology（江苏省机载探测与智能感知技术工程研究中心）
Hyperspectral Image Denoising and Anomaly Detection Based on Low-rank and Sparse Representations
Hong Kong Baptist University（香港浸会大学）；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）；Shenzhen University（深圳大学）；Instituto de Telecomunicações（电信研究院）；Instituto Superior Técnico（里斯本高等技术学院）；Universidade de Lisboa（里斯本大学）
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
National Laboratory of Radar Signal Processing, Xidian University（西安电子科技大学雷达信号处理国家重点实验室）；Science and Technology on Multispectral Information Processing Laboratory, School of Automation, Huazhong University of Science and Technology（华中科技大学自动化学院多谱信息处理技术国家级重点实验室）
Bayesian Fully-Connected Tensor Network for Hyperspectral-Multispectral Image Fusion
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；School of Computer Science and Artificial Intelligence, Zhengzhou University（郑州大学计算机科学与人工智能学院）；Department of Computer Science, St. Francis Xavier University（圣弗朗西斯大学计算机科学系）
Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function
Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（地理信息中心，深圳先进技术研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application（深圳海洋环境大数据分析与应用工程实验室）
Correcting Spectra Outside the Backbone: A Model-Agnostic Rectifier for Hyperspectral Image Super-Resolution
School of Computer Science and Technology, Xi’an Jiaotong University（计算机科学与技术学院，西安交通大学）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院，合肥工业大学）
Self-Supervised Super-Resolution for Sentinel-5P Hyperspectral Images
Université d’Orléans, Université de Tours, CNRS, IDP, UMR 7013（奥尔良大学、图尔大学、法国国家科学研究中心、IDP、UMR 7013）；Faculty of Mathematical Sciences, University of Khartoum（数学科学学院，喀土穆大学）；ENS Lyon, France（里昂高等师范学院，法国）；Laboratory of Physics and Chemistry of the Environment and Space (LPC2E), CNRS UMR 7328, University of Orléans, France（环境与空间物理化学实验室（LPC2E），法国国家科学研究中心UMR 7328，奥尔良大学，法国）；Institut universitaire de France (IUF), France（法国国家研究院（IUF），法国）
Interpretable Landsat-to-Hyperspectral Dual Super-Resolution Without Large Matrix Inversion
National Cheng Kung University (NCKU)（国立成功大学）；Agency of Rural Development and Soil and Water Conservation (ARDSWC), Ministry of Agriculture, Executive Yuan（行政院农业委员会农村发展及水土保持署）
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；College of Computer Science, Nankai University（南开大学计算机学院）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Peng Cheng Laboratory（鹏城实验室）
Recovering Cloud Microstructures with Cascaded Diffusion Inversion
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Hebrew University of Jerusalem（耶路撒冷希伯来大学）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
Deep Clustering With Intra-class Distance Constraint for Hyperspectral Images
Liaoning Technical University（辽宁工程技术大学）；Haixi Institute, Chinese Academy of Sciences（中国科学院海西研究院）；Technical University of Munich（慕尼黑工业大学）；Hong Kong Polytechnic University（香港理工大学）
HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model
College of Oceanography and Space Informatics, China University of Petroleum (East China)（中国石油大学（华东）海洋与空间信息学院）；National Subsea Centre, Robert Gordon University（罗伯特戈登大学国家海底中心）；Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心亥姆霍兹弗赖贝格资源技术研究所）
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
Tsinghua University（清华大学）；Shenzhen Institute of Future Media Technology（深圳未来媒体技术研究院）；Westlake University（西湖大学）；ETH Zürich（苏黎世联邦理工学院）；University of Würzburg（维尔茨堡大学）
