Inverse Problems

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20 papers in the last four weeks, up 186% on the four weeks before. 0.2% of all new papers.

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Enabling self-supervised learned primal dual with Noise2Inverse
Jun 25, 2026Antti Sällinen, Siiri Rautio, Santeri Kaupinmäki +1Self-Supervised Learning Image Reconstruction
Research Unit of Mathematical Sciences, University of Oulu, Finland · Department of Mathematics and Information Science, Josai University, Japan · Department of Computer Science, University College London, United Kingdom
Dual-Prior Guided Null-Space Learning with Mixture-of-Splines for Arbitrary Medical Slice Super-Resolution
Jun 25, 2026Haofei Song, Siyuan Xu, Xintian Mao +3Image Reconstruction Medical Image Super-Resolution
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
Hybrid deep learning-based phase diversity method for wavefront reconstruction
Jun 24, 2026Y. Rodimkov, A. Kotov, K. Burdonov +4Quasi-Newton Methods Inverse Problems
Lobachevskii State University of Nizhni Novgorod, pr. Gagarina 23, Nizhni Novgorod, 603022 Russia · Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod 603951, Russia
GPU-Accelerated Inverse Structural Anastylosis from Block Collapse Dynamics
Jun 23, 2026L. A. Muñoz3D Reconstruction Cultural Heritage
Computational Robotics Lab School of Engineering and Sciences Tecnológico de Monterrey Monterrey, Nuevo León, Mexico
Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching
Jun 23, 2026Peiyan Hu, Jian Zhang, Jiashu Pan +6Simulation-Based Inference Conditional Flow Matching
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. · Department of Artificial Intelligence, School of Engineering, Westlake University, Hangzhou, Zhejiang, 310030, China. · School of Astronomy and Space Sciences, Chinese Academy of Sciences, Beijing, 100049, China. +4
Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications
Jun 23, 2026Arindam Sengupta, Paul Jeanney, Ricardo Vinuesa +2Sparse Recovery Tensor Decomposition
ETSI Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Plaza Cardenal Cisneros, 3, Madrid, 28040, Spain · Ove Arup, C. de Alfonso XI, 12, Retiro, Madrid, 28014, Spain · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109, United States
Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction
Jun 23, 2026Antoine De Paepe, Alexandre Bousse, Dimitris VisvikisImage Reconstruction Sparse-View CT Reconstruction
LaTIM, Inserm, UMR 1101, Université de Bretagne Occidentale, Brest, France
Geological text descriptions in ill-posed inverse problems: insights from learned hydraulic-conductivity inversion
Jun 23, 2026Taiga Saito, Yu Otake, Daijiro Mizutani +1PDE Inverse Problems Inverse Problems
Department of Civil and Environmental Engineering, Tohoku University, 6-6-06 Aramaki Aoba, Aoba-ku, Sendai, Miyagi, 980-8579, Japan
Tensor Train Decomposition-based 3D Implicit Full Waveform Inversion with Multi-scale Structural Similarity
Jun 22, 2026Liangsheng He, Chao Song, Tiansheng Chen +2Implicit Neural Representations 3D Reconstruction
State Key Laboratory of Deep Earth Exploration and Imaging College of GeoExploration Science and Technology, Jilin University Changchun, Jilin, 130021, China · State Key Laboratory of DeepChaoEarthSong Exploration and Imaging College of GeoExploration Science and Technology, Jilin University Changchun, Jilin, 130021, China · Petroleum Exploration and Production Research Institute, Sinopec Beijing, 100083, China
NullFlow: One-Step Generative Reconstruction
Jun 21, 2026Xiao Shi, Edward P. Chandler, Chicago Y. Park +2Flow Matching Image Reconstruction
Computational Imaging Group (CIG) · Washington University in St. Louis
Patched Flow Matching: Generative Wall-Pressure Reconstruction Beyond Training-Domain Scales from Sparse Sensors
Jun 20, 2026Meet Hemant Parikh, Yi Liu, Jian-Xun WangFlow Matching Generative Modeling
aSibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY · bDepartment of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN
Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network
Jun 19, 2026Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie +1Uncertainty Quantification Bayesian Inverse Problems
Japan Agency for Marine-Earth Science and Technology · Earth Science New Zealand
Modeling Branches for Active Manipulation using Iterative Parameter Estimation
Jun 17, 2026Madhav Rijal, Rashik Shrestha, Trevor Smith +1Deformable Object Manipulation Robotic Manipulation
Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26505, USA
Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery
Jun 16, 2026Onur Efe, Arkadas OzakinInverse Problems Image Inverse Problems
Physics Department, Bogazici University
Exact Posterior Score Estimation for Solving Linear Inverse Problems
Jun 15, 2026Abbas Mammadov, Ozgur Kara, Kaan Oktay +5Diffusion Models for Image Restoration Diffusion-Based Inverse Problems
University of Oxford · UIUC · fal +1
Latent Space Reinforcement Learning for Inverse Material Estimation in Food Fracture Simulation
Jun 15, 2026Adrian Ramlal, Yuhao Chen, John S. ZelekParameter Estimation Reinforcement Learning
University of Waterloo
EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Video
Jun 15, 2026Hyunjin Kim, Ri-Zhao Qiu, Guangqi Jiang +1Deformable Object Manipulation Deformable Shape Reconstruction
UC San Diego
High-Fidelity 4D Hand-Object Capture via Multi-View Spatiotemporal Tracking and Physics-Aware Gaussians
Jun 14, 2026Bo Peng, Xu Chen, Yi Gu +64D Reconstruction 3D Hand Pose Estimation
Google XR · The Hong Kong University of Science and Technology (Guangzhou) · University of Science and Technology of China (USTC)
Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations
Jun 12, 2026Hyeonbin Moon, Donghyuk Cho, Jecheon Yu +2Parameter Identifiability Physics-Informed ML
Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · School of Engineering, Deakin University, 75 Pigdons Road, Waurn Ponds, Victoria, 3216, Australia · KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea +1
Decoupled Latent Optimization of Diffusion Models for Full Waveform Inversion
Jun 12, 2026Chen Min, Zheng MaDiffusion-Based Inverse Problems PDE Inverse Problems
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China · CMA-Shanghai, Shanghai Jiao Tong University, Shanghai, China
Full-Self Diagnostics (FSD): Physics-Grounded Visual Biomarker Inference from Smartphone Video via Inverse Problems and Operator Learning
Jun 11, 2026Jonathan Thomas, Harsh ThakerPhysics-Informed ML Inverse Problems
Algomash® (Algorithmic Mashup Inc.)
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models
Jun 8, 2026Conor RowanScientific Discovery Scientific ML
Aerospace Engineering2026 University of Colorado Boulder 3775 Discovery DriveJun 9 Boulder, CO 80309
Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing
Jun 3, 2026Monika Stipsitz, Hèlios Sanchis-Alepuz, Jacob Reynvaan +1Synthetic Data Generation Scientific ML
Embedded Systems Silicon Austria Labs GmbH Graz, Austria
Measurement Geometry and Design for Trustworthy Generative Inverse Problems
Jun 1, 2026Pengfei Jin, Na Li, Quanzheng LiComputational Imaging Accelerated MRI Reconstruction
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 · School of Engineering and Applied Sciences, Harvard University, Boston, MA 02138
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
May 31, 2026Hoang Anh Nguyen, Divakar Vashisth, Ali TuraHybrid Quantum-Classical ML Physics-Informed ML
Department of Geophysics, Colorado School of Mines, Golden, CO 80401 USA · Department of Energy Science and Engineering, Stanford University, Stanford, CA 94305 USA · Department of Petroleum Engineering, Colorado School of Mines, Golden, CO 80401 USA
(HB-ARFM) History-Bootstrapped Flow Matching for Inverse Boiling Reconstruction
May 29, 2026Xianwei Zou, Sheikh Md Shakeel Hassan, Arthur Feeney +1Flow Matching PDE Inverse Problems
Department of Electrical Engineering and Computer Science, University of California Irvine, USA.
Perturbative methods for non-parametric instrumental variable
May 29, 2026Wei Bu, Arthur GrettonKernel Ridge Regression Instrumental Variable Estimation
Harvard University, Northeastern University, Physics Department · University College London.
KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems
May 29, 2026Alireza Kheirandish, Jihoon Hong, Sara Fridovich-KeilDiffusion-Based OOD Detection Distribution Shift Detection
Georgia Institute of Technology
Zeroth-Order Non-Log-Concave Sampling with Variance Reduction and Applications to Inverse Problems
May 28, 2026M. Berk Sahin, Behzad Sharif, Abolfazl HashemiPosterior Sampling Variance Reduction
Elmore School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA · Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA
SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data
May 28, 2026Joseph Stitt, Pratik Rathore, Madeleine Udell +1Synthetic Data Generation PDE Surrogate Modeling
Stanford University

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Enabling self-supervised learned primal dual with Noise2Inverse

Antti Sällinen, Siiri Rautio, Santeri Kaupinmäki, Andreas Hauptmann

Research Unit of Mathematical Sciences, University of Oulu, Finland · Department of Mathematics and Information Science, Josai University, Japan · Department of Computer Science, University College London, United Kingdom

Self-Supervised Learning Image Reconstruction Sparse-View CT Reconstruction Computed Tomography Inverse Problems

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X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are noisy and incomplete. While learned reconstruction methods such as the Learned Primal-Dual algorithm achieve strong performance, they typically rely on supervised training with access to ground-truth data, which is often unavailable in practice. In this work, we propose a self-supervised reconstruction method by extending the Noise2Inverse framework to the Learned Primal-Dual algorithm. The resulting approach, called Noise2Inverse Learned Primal-Dual (N2I-LPD), enables training of a learned iterative reconstruction operator without ground-truth images by exploiting the statistical independence of noise in distinct measurements with respect to angular rotation of the CT-scan. We compare the proposed method with classical reconstruction methods, as well as neural network-based approaches such as a U-Net trained within the same N2I framework. The results demonstrate that N2I-LPD achieves improved reconstruction quality, highlighting the potential of combining learned reconstruction operators with self-supervised training strategies for practical CT imaging scenarios where ground-truth data is unavailable.

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Latest papers 161