In large-scale spatial surveys, such as the forthcoming ESA Euclid mission, images may be undersampled due to the optical sensors sizes. Therefore, one may consider using a super-resolution (SR) method to recover aliased frequencies, prior to further analysis. This is particularly relevant for point-source images, which provide direct measurements of the instrument point-spread function (PSF). We introduce SPRITE, SParse Recovery of InsTrumental rEsponse, which is an SR algorithm using a sparse analysis prior. We show that such a prior provides significant improvements over existing methods, especially on low SNR PSFs.
Image Restoration with Point Spread Function Regularization and Active Learning
College of Electronic Information and Optical Engineering, Taiyuan（太原电子信息与光学工程学院）；Peng Cheng Lab, Shenzhen（鹏城实验室）；Department of Physics, Durham University（杜伦大学物理系）；National Astronomical Observatories, Beijing（中国科学院国家天文台）；Yunnan Observatories, Kunming（中国科学院云南天文台）
Novel Sparse Recovery Algorithms for 3D Debris Localization using Rotating Point Spread Function Imagery
Chinese Univ. of Hong Kong（香港中文大学）；Wake Forest Univ.（维克森林大学）；Univ. Minnesota（明尼苏达大学）；Univ. New Mexico（新墨西哥大学）；ENS Cachan, Univ. Paris-Saclay（巴黎萨克雷大学卡尚高等师范学校）
Low-Rank Tensor Recovery via Variational Schatten-p Quasi-Norm and Jacobian Regularization
School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）；School of Control Science and Engineering, Dalian University of Technology（控制科学与工程学院，大连理工大学）；Tsinghua University（清华大学）
Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
School of Mathematics and Statistics, Southwest University（西南大学数学与统计学院）；Macau Institute of Systems Engineering, Macau University of Science and Technology（澳门科技大学澳门系统工程研究所）
