In observational astronomy, noise obscures signals of interest. Large-scale astronomical surveys are growing in size and complexity, which will produce more data and increase the workload of data processing. Developing automated tools, such as convolutional neural networks (CNN), for denoising has become a promising area of research. We investigate the feasibility of CNN-based self-supervised learning algorithms (e.g., Noise2Noise) for denoising astronomical images. We experimented with Noise2Noise on simulated noisy astronomical data. We evaluate the results based on the accuracy of recovering flux and morphology. This algorithm can well recover the flux for Poisson noise ( $98.13${\raisebox{0.5ex}{\tiny$^{+0.77}_{-0.90} $}$\large\%$}) and for Gaussian noise when image data has a smooth signal profile ($96.45${\raisebox{0.5ex}{\tiny$^{+0.80}_{-0.96} $}$\large\%$}).
maria goes NIFTy: Gaussian Process-Based Reconstruction and Denoising of Simulated (Sub-)Millimetre Single-Dish Telescope Data
Technical University of Munich（慕尼黑工业大学）；Excellence Cluster ORIGINS（ORIGINS卓越集群）；Leiden Observatory, Leiden University（莱顿大学莱顿天文台）；Yale University（耶鲁大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；European Southern Observatory（欧洲南方天文台）；Institute of Space Sciences (ICE, CSIC)（空间科学研究所（ICE，CSIC））
Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China（自动化系，北京信息科学与技术国家研究中心，清华大学，北京，中国）；Department of Astronomy, Tsinghua University, Beijing, China（天文系，清华大学，北京，中国）；Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China（脑与认知科学研究院，清华大学，北京，中国）；Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo, Kashiwa, Japan（宇宙物理与数学科学研究所（WPI），东京大学，日光，日本）；National Astronomical Observatory of Japan, Tokyo, Japan（日本国家天文台，东京，日本）；Institute for Cosmic Ray Research, The University of Tokyo, Kashiwa, Japan（宇宙射线研究所，东京大学，日光，日本）；College of AI, Tsinghua University, Beijing, China（人工智能学院，清华大学，北京，中国）；School of Mathematics and Physics, Qinghai University, Xining, China（数学与物理学院，青海大学，西宁，中国）；Tsinghua Wuxi Institute of Applied Technology, Wuxi, China（清华大学无锡应用技术研究院，无锡，中国）
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
National Research Council Herzberg Astronomy an Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada（国家研究委员会赫兹伯格天文学与天体物理学研究中心，5071号西萨安奇路，维多利亚，BC V9E 2E7，加拿大）；Department of Computer Science, , University of British Columbia, Vancouver, BC, V6T 1Z4, Canada（计算机科学系，不列颠哥伦比亚大学，温哥华，BC，V6T 1Z4，加拿大）
AstroClearNet: Deep image prior for multi-frame astronomical image restoration
Department of Applied Mathematics and Statistics, Johns Hopkins University（应用数学与统计学系，约翰霍普金斯大学）；Johns Hopkins University（约翰霍普金斯大学）；Department of Computer Science, Johns Hopkins University（计算机科学系，约翰霍普金斯大学）；Department of Physics and Astronomy, Johns Hopkins University（物理与天文学系，约翰霍普金斯大学）
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Guangzhou University（广州大学）
