Self-Supervised Image Denoising

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Self-supervised image denoising aims to recover image detail from noisy data when clean reference images are unavailable or difficult to obtain. Common approaches train on independently noisy observations, hide pixels or patches with blind-spot and masking methods, or generate new training pairs through noise models, physics, and test-time adaptation. These methods are used in natural photography and scientific imaging, including CT, MRI, ultrasound, astronomy, and hyperspectral imaging, where noise often has modality-specific structure. A central challenge is matching the training assumptions to real noise while suppressing artifacts without erasing fine detail; improvements in PSNR or SSIM may not guarantee better clinical or scientific interpretation.

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25 papers5 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Matching Particle-Arrival Rates for Low-Count Image Denoising
Oct 23, 2026Huazhen Liu, Zipei Yan, Jizhou LiImage Denoising Self-Supervised Image Denoising
Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.
Self-Supervised Multibeam Sonar Restoration via Coherent Forward Modeling
Oct 22, 2026Zixin Wang, James Hopgood, Mike DaviesImage Restoration Physics-Guided Image Restoration
School of Engineering, University of Edinburgh, Edinburgh EH9 3FG, U.K.
Denoise X: Self-Supervised U-Net for Quantum Mottle Suppression in Chest Radiography
Oct 13, 2026Parth Giramkar, Aryan Patle, Vanita KshirsagarImage Denoising Self-Supervised Image Denoising
Department of Artificial Intelligence and Data Science, Dr. D. Y. Patil Institute of Technology, Pune, Maharashtra 411018, India
$\mathrm{S}^3$ -PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors
Oct 13, 2026Yukun Cui, Jiangshe Zhang, Shuang Xu +6Image Restoration Image Denoising
Xi’an Jiaotong University · Northwestern Polytechnical University · Nanyang Technological University +3
Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
Oct 13, 2026Langrui Zhou, Guang Li, Xinyu Huang +5Self-Supervised Image Denoising LDCT Denoising
Jiangsu Key Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210009, China. · Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 201203, China · Jiangsu Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Institute of Clinical Medicine, Nanjing Drum Tower Hospital, Medical School, Nanjing University, Nanjing 210093, China
Mask2Restore: Self-Supervised Ultrasound Despeckling via Inpainting
Sep 26, 2026Xuesong Li, Yingtai Xu, Zhongliang Jiang +2Image Restoration Image Denoising
Computer Aided Medical Procedures (CAMP), Technical University of Munich, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China
Beyond MSE: Rician Likelihood Denoising for Self-Supervised Cardiac $T2$ and $T1ρ$ MRI
Sep 24, 2026Nicholas A. Jacobs, Jason Mendes, Ravi Ranjan +2Self-Supervised Image Denoising Magnetic Resonance Imaging
University of Utah, Salt Lake City UT 84112, USA
One-Step Is Optimal: Unconditional Rectified Flows are Noise2Noise Denoisers, and Multi-Step Integration Provably Hurts---A Benchmark and Task-Based Detectability Study on Low-Dose CT
Sep 15, 2026Timothy Sereda, Debesh JhaSelf-Supervised Image Denoising Rectified Flow
Department of Computer Science Biomedical Perception & Intelligence Lab University of South Dakota Vermillion, South Dakota, USA
Noise2Noise Revisited: Training Pair Distributions Dominate Loss Choice in Self-Supervised Denoising
Sep 15, 2026Dingyan Shang, Zhenyu Xu, Youting Wang +2Self-Supervised Image Denoising Denoising Autoencoders
Independent Researcher · Frisco, USA · Fulshear, USA +3
Physics-Driven Independent Pair Generation for Iterative Self-Supervised Low-Dose CT Denoising
Sep 1, 2026Xianlei Han, Shaoyu Wang, Jiancheng Fang +2Self-Supervised Image Denoising Self-Supervised Learning
School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China · School of Information Engineering, Nanchang University, Nanchang 330031, China · School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 510275, Guangdong, China
Self-Supervised Noise2Noise-Enhanced Denoising for Continuous-Scan Air-Plasma THz Spectroscopy
Aug 17, 2026Adam Umra, Oways Alsoloh, Oliver Nagy +2Self-Supervised Image Denoising
Institute of Digital Communication Systems, Ruhr University Bochum, Germany · Photonics and Ultrafast Laser Science, Ruhr University Bochum, Germany
LoTA-N2N: Local Trace Adaptation for Zero-Shot Self-Supervised Image Denoising
Jul 27, 2026Jintong Hu, Bin Xia, Junlin Liu +2Self-Supervised Image Denoising Self-Supervised Learning
Tsinghua University · The Chinese University of Hong Kong · University of Southern California +1
Denoising ICF Images with Multiplicative Uniform Noise: A Self-Supervised Study Based on the Log-Domain Noisier2Inverse Framework
Jun 26, 2026Gyeongha Hwang, Bradley Thomas Wolfe, Naima NaheedSelf-Supervised Image Denoising Self-Supervised Learning
Department of Mathematics, Yeungnam University, Gyeongsan, Republic of Korea. · Research Technologist, P-4: Thermonuclear Plasma Physics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. · Department of Computer Science and Engineering, Benedict College, Columbia, SC 29204, USA.
NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling
Jun 22, 2026Jaehyun Cho, YoungJoon YooSelf-Supervised Image Denoising 3D Medical Imaging
Department of Artificial Intelligence, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea · SNUAILAB, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization
May 23, 2026Swapna Pillai, Siddharth Singh Savner, Sujit Kumar SahooSelf-Supervised Image Denoising Self-Supervised Learning
School of Electrical Sciences, Indian Institute of Technology Goa, Goa 403401, India · Inria, Sophia Antipolis, France
Coherence-Guided Pyramid Self-Contrastive Disentanglement for Ultrasound Imaging via Single-Shot Test-Time Training
May 12, 2026Jiajing Zhang, Bingze Dai, Xi Zhang +2Self-Supervised Image Denoising Test-Time Adaptation
Department of Electrical and Computer Engineering, The University of Hong Kong, Hong Kong. · Department of Biomedical Engineering, Duke University, North Carolina, United States.
Deploying Self-Supervised Learning for Real Seismic Data Denoising
May 11, 2026Giovanny A. M. Arboleda, Claudio D. T. de Souza, Carlos E. M. dos Anjos +6Self-Supervised Image Denoising Self-Supervised Learning
COPPE Federal University of Rio de Janeiro Rio de Janeiro, Brazil · CENPES, Petrobras Rio de Janeiro, Brazil
FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoising
May 11, 2026Guoquan Wei, Liu Shi, Chong Chen +1Self-Supervised Image Denoising Self-Supervised Learning
School of Information Engineering, Nanchang University, Nanchang 330031, China · SKLMS, ICMSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning
May 7, 2026Qiyu Rao, Haozhe Tian, Homayoun Hamedmoghadam +1Self-Supervised Image Denoising Electroencephalography
Department of Electrical and Electronic Engineering, Imperial College London · Dyson School of Design Engineering, Imperial College London
Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT
May 1, 2026Johannes B. Thalhammer, Lorenzo D'Amico, Lucy Costello +11Self-Supervised Image Denoising Computed Tomography
Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, TU Munich · Munich Institute of Biomedical Engineering, TU Munich · Institute for Diagnostic and Interventional Radiology, TUM Klinikum +3
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
Apr 18, 2026Omid Vaheb, Sebastien Fabbro, Stark DraperSelf-Supervised Image Denoising Noise Robustness
Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON M5S 3G4, Canada · National Research Council Herzberg Astronomy an Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada · Department of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising
Apr 9, 2026Panagiotis Gkotsis, Athanasios A. RontogiannisDeep Image Prior Image Denoising
Robotics Institute, Athena Research and Innovation Center, 151 25 Maroussi, Greece · School of Electrical and Computer Engineering, National Technical University of Athens, 157 80 Athens, Greece
TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoising
Apr 6, 2026Junyoung Park, Youngjin Oh, Nam Ik ChoSelf-Supervised Image Denoising Image Denoising
Department of ECE, INMC, Seoul National University, South Korea · IPAI, Seoul National University, South Korea
Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising
Mar 26, 2026Brayan Monroy, Jorge Bacca, Julián TachellaSelf-Supervised Image Denoising Self-Supervised Learning
Universidad Industrial de Santander, Colombia · CNRS, ENS de Lyon, France
Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Feb 19, 2026Yuduo Guo, Hao Zhang, Mingyu Li +10Image Denoising Self-Supervised Image Denoising
Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China · Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China · Department of Astronomy, Tsinghua University, Beijing, China +6

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Matching Particle-Arrival Rates for Low-Count Image Denoising

Huazhen Liu, Zipei Yan, Jizhou Li

Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong.

Image Denoising Self-Supervised Image Denoising

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Low-count imaging arises when limited illumination or dose yields only a few detected particles in microscopy, such as photons or electrons, causing measurements to be dominated by signal-dependent Poisson shot noise. Supervised denoisers typically require paired clean and noisy observations, whereas many self-supervised approaches exploit spatial or temporal redundancy without directly estimating the underlying particle-arrival process. We introduce PARM, a self-supervised denoising framework based on particle-arrival-rate matching. By modeling particle accumulation over exposure as a Poisson counting process, PARM identifies the conditional particle-arrival-rate field with the posterior mean of the latent clean rate. Poisson thinning then splits each observation into complementary lower-exposure views that provide self-supervised rate targets for one another. In implementation, the network estimates the normalized spatial allocation of the conditional rate, while the observed total count determines its global particle mass. Experiments on synthetic data, natural-video sequences corrupted with Poisson noise, and real low-dose electron microscopy demonstrate leading or competitive performance while effectively preserving fine structures. PARM therefore provides a statistically grounded approach to low-count image denoising when clean references are unavailable.