Analysis and optimization of an algorithm for discrete tomography. - Kees Joost Batenburg (Electron. Notes Discret. Math., 2003)

On the Reconstruction of Crystals Through Discrete Tomography. - Kees Joost Batenburg; Willem Jan Palenstijn (IWCIA, 2004)

Neural Networks for Discrete Tomography. - Kees Joost Batenburg; Walter A. Kosters (BNAIC, 2005)

An evolutionary algorithm for discrete tomography. - Kees Joost Batenburg (Discret. Appl. Math., 2005)

A new algorithm for 3D binary tomography. - Kees Joost Batenburg (Electron. Notes Discret. Math., 2005)

A Network Flow Algorithm for Binary Image Reconstruction from Few Projections. - Kees Joost Batenburg (DGCI, 2006)

A Learning Classifier Approach to Tomography. - Kees Joost Batenburg (ECAI, 2006)

A Neural Network Approach to Real-Time Discrete Tomography. - Kees Joost Batenburg; Walter A. Kosters (IWCIA, 2006)

Optimal Threshold Selection for Tomogram Segmentation by Reprojection of the Reconstructed Image. - Kees Joost Batenburg; Jan Sijbers (CAIP, 2007)

Dart: A Fast Heuristic Algebraic Reconstruction Algorithm for Discrete Tomography. - Kees Joost Batenburg; Jan Sijbers (ICIP (4), 2007)

Automatic multiple threshold scheme for segmentation of tomograms. - Kees Joost Batenburg; Jan Sijbers (Image Processing, 2007)

A Network Flow Algorithm for Reconstructing Binary Images from Discrete X-rays. - Kees Joost Batenburg (J. Math. Imaging Vis., 2007)

Selection of Local Thresholds for Tomogram Segmentation by Projection Distance Minimization. - Kees Joost Batenburg; Jan Sijbers (DGCI, 2008)

Threshold Selection for Segmentation of Dense Objects in Tomograms. - Wim van Aarle; Kees Joost Batenburg; Jan Sijbers (ISVC (1), 2008)

A Reasoning Framework for Solving Nonograms. - Kees Joost Batenburg; Walter A. Kosters (IWCIA, 2008)

Fast bias field reduction by localized Lloyd-Max quantization. - Rudolf Hanel; Kees Joost Batenburg; Steve De Backer; Paul Scheunders; Jan Sijbers (Image Processing, 2008)

A Network Flow Algorithm for Reconstructing Binary Images from Continuous X-rays. - Kees Joost Batenburg (J. Math. Imaging Vis., 2008)

Preface. - Kees Joost Batenburg; Antal Nagy; Maurice Nivat (Theor. Comput. Sci., 2008)

In Memoriam Attila Kuba (1953-2006). - Kees Joost Batenburg; Antal Nagy; Maurice Nivat (Theor. Comput. Sci., 2008)

Grey Level Estimation for Discrete Tomography. - Kees Joost Batenburg; Wim van Aarle; Jan Sijbers (DGCI, 2009)

Experiences with Cell-BE and GPU for Tomography. - Sander van der Maar; Kees Joost Batenburg; Jan Sijbers (SAMOS, 2009)

Generic iterative subset algorithms for discrete tomography. - Kees Joost Batenburg; Jan Sijbers (Discret. Appl. Math., 2009)

Solving Nonograms by combining relaxations. - Kees Joost Batenburg; Walter A. Kosters (Pattern Recognit., 2009)

Adaptive thresholding of tomograms by projection distance minimization. - Kees Joost Batenburg; Jan Sijbers (Pattern Recognit., 2009)

Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization. - Kees Joost Batenburg; Jan Sijbers (IEEE Trans. Medical Imaging, 2009)

A discrete tomography approach for superresolution micro-CT images: application to bone. - Gert Van Gompel; Kees Joost Batenburg; Elke Van de Casteele; Wim van Aarle; Jan Sijbers (ISBI, 2010)

An Algebraic Framework for Discrete Tomography: Revealing the Structure of Dependencies. - Arjen Stolk; Kees Joost Batenburg (SIAM J. Discret. Math., 2010)

Error Bounds on the Reconstruction of Binary Images from Low Resolution Scans. - Wagner Fortes; Kees Joost Batenburg (CAIP (1), 2011)

Bounds on the Difference between Reconstructions in Binary Tomography. - Kees Joost Batenburg; Wagner Fortes; Lajos Hajdu; Robert Tijdeman (DGCI, 2011)

Three-Dimensional Reconstruction of a Nanoparticle at Atomic Resolution. - Kees Joost Batenburg; Sandra Van Aert (ERCIM News, 2011)

A semi-automatic algorithm for grey level estimation in tomography. - Kees Joost Batenburg; Wim van Aarle; Jan Sijbers (Pattern Recognit. Lett., 2011)

DART: A Practical Reconstruction Algorithm for Discrete Tomography. - Kees Joost Batenburg; Jan Sijbers (IEEE Trans. Image Process., 2011)

Optimal Threshold Selection for Segmentation of Dense Homogeneous Objects in Tomographic Reconstructions. - Wim van Aarle; Kees Joost Batenburg; Jan Sijbers (IEEE Trans. Medical Imaging, 2011)

Combined Motion Estimation and Reconstruction in Tomography. - Geert Van Eyndhoven; Jan Sijbers; Kees Joost Batenburg (ECCV Workshops (1), 2012)

A Central Reconstruction Based Strategy for Selecting Projection Angles in Binary Tomography. - Péter Balázs; Kees Joost Batenburg (ICIAR (1), 2012)

On the Difficulty of Nonograms. - Kees Joost Batenburg; Walter A. Kosters (J. Int. Comput. Games Assoc., 2012)

Automatic Parameter Estimation for the Discrete Algebraic Reconstruction Technique (DART). - Wim van Aarle; Kees Joost Batenburg; Jan Sijbers (IEEE Trans. Image Process., 2012)

Fast Approximation of Algebraic Reconstruction Methods for Tomography. - Kees Joost Batenburg; Linda Plantagie (IEEE Trans. Image Process., 2012)

A Method for Feature Detection in Binary Tomography. - Wagner Fortes; Kees Joost Batenburg (DGCI, 2013)

Discrete representation of local orientation in grains using diffraction contrast tomography. - Nicola Viganò; Wolfgang Ludwig; Kees Joost Batenburg (ISPA, 2013)

Automatic Optimization of Alignment Parameters for Tomography Datasets. - Folkert Bleichrodt; Kees Joost Batenburg (SCIA, 2013)

Dynamic angle selection in binary tomography. - Kees Joost Batenburg; Willem Jan Palenstijn; Péter Balázs; Jan Sijbers (Comput. Vis. Image Underst., 2013)

Bounds on the quality of reconstructed images in binary tomography. - Kees Joost Batenburg; Wagner Fortes; Lajos Hajdu; Robert Tijdeman (Discret. Appl. Math., 2013)

Approximate Discrete Reconstruction Algorithm. - Kees Joost Batenburg; Wagner Fortes; Robert Tijdeman (Fundam. Informaticae, 2013)

Discrete Tomography in MRI: a Simulation Study. - Hilde Segers; Willem Jan Palenstijn; Kees Joost Batenburg; Jan Sijbers (Fundam. Informaticae, 2013)

Discrete algebraic reconstruction technique: a new approach for superresolution reconstruction of license plates. - Karim Zarei Zefreh; Wim van Aarle; Kees Joost Batenburg; Jan Sijbers (J. Electronic Imaging, 2013)

Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks. - Daniël Maria Pelt; Kees Joost Batenburg (IEEE Trans. Image Process., 2013)

Adaptive Grid Refinement for Discrete Tomography. - Tristan van Leeuwen; Kees Joost Batenburg (DGCI, 2014)

SDART: An algorithm for discrete tomography from noisy projections. - Folkert Bleichrodt; Frank Tabak; Kees Joost Batenburg (Comput. Vis. Image Underst., 2014)

The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography. - Tom Roelandts; Kees Joost Batenburg; Arnold J. den Dekker; Jan Sijbers (Comput. Vis. Image Underst., 2014)

Aligning Projection Images from Binary Volumes. - Folkert Bleichrodt; Jan De Beenhouwer; Jan Sijbers; Kees Joost Batenburg (Fundam. Informaticae, 2014)

Approximating Algebraic Tomography Methods by Filtered Backprojection: A Local Filter Approach. - Linda Plantagie; Kees Joost Batenburg (Fundam. Informaticae, 2014)

Super-Resolution for Computed Tomography Based on Discrete Tomography. - Wim van Aarle; Kees Joost Batenburg; Gert Van Gompel; Elke Van de Casteele; Jan Sijbers (IEEE Trans. Image Process., 2014)

Region-Based Iterative Reconstruction of Structurally Changing Objects in CT. - Geert Van Eyndhoven; Kees Joost Batenburg; Jan Sijbers (IEEE Trans. Image Process., 2014)

Improving Filtered Backprojection Reconstruction by Data-Dependent Filtering. - Daniël Maria Pelt; Kees Joost Batenburg (IEEE Trans. Image Process., 2014)

Region based 4D tomographic image reconstruction: Application to cardiac x-ray CT. - Geert Van Eyndhoven; Kees Joost Batenburg; Jan Sijbers (ICIP, 2015)

Filtered backprojection using algebraic filters; application to biomedical micro-CT data. - Linda Plantagie; Wim van Aarle; Jan Sijbers; Kees Joost Batenburg (ISBI, 2015)

Quality bounds for binary tomography with arbitrary projection matrices. - Wagner Fortes; Kees Joost Batenburg (Discret. Appl. Math., 2015)

Algebraic filter approach for fast approximation of nonlinear tomographic reconstruction methods. - Linda Plantagie; Kees Joost Batenburg (J. Electronic Imaging, 2015)

An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging. - Geert Van Eyndhoven; Kees Joost Batenburg; Daniil Kazantsev; Vincent Van Nieuwenhove; Peter D. Lee; Katherine J. Dobson; Jan Sijbers (IEEE Trans. Image Process., 2015)

Mobile Radio Tomography: Agent-Based Imaging. - Kees Joost Batenburg; Leon S. Helwerda; Walter A. Kosters; Tim van der Meij (BNCAI, 2016)

A method for locally approximating regularized iterative tomographic reconstruction methods. - Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2016)

An Orientation-space Super Sampling Technique for Six-dimensional Diffraction Contrast Tomography. - Nicola Viganò; Kees Joost Batenburg; Wolfgang Ludwig (Fundam. Informaticae, 2016)

Easy implementation of advanced tomography algorithms using the ASTRA toolbox with Spot operators. - Folkert Bleichrodt; Tristan van Leeuwen; Willem Jan Palenstijn; Wim van Aarle; Jan Sijbers; Kees Joost Batenburg (Numer. Algorithms, 2016)

TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation. - Xiaodong Zhuge; Willem Jan Palenstijn; Kees Joost Batenburg (IEEE Trans. Image Process., 2016)

A Parametric Level-Set Method for Partially Discrete Tomography. - Ajinkya Kadu; Tristan van Leeuwen; Kees Joost Batenburg (DGCI, 2017)

High-Level Algorithm Prototyping: An Example Extending the TVR-DART Algorithm. - Axel Ringh; Xiaodong Zhuge; Willem Jan Palenstijn; Kees Joost Batenburg; Ozan Öktem (DGCI, 2017)

Computational Imaging - Introduction to the Special Theme. - Kees Joost Batenburg; Tamás Szirányi (ERCIM News, 2017)

A Novel Tomographic Reconstruction Method Based on the Robust Student's t Function For Suppressing Data Outliers. - Daniil Kazantsev; Folkert Bleichrodt; Tristan van Leeuwen; Anders Kaestner; Philip J. Withers; Kees Joost Batenburg; Peter D. Lee (IEEE Trans. Computational Imaging, 2017)

A Multi-channel DART Algorithm. - Mathé T. Zeegers; Felix Lucka; Kees Joost Batenburg (IWCIA, 2018)

Computational 3D X-ray Imaging for the Food Industry. - Kees Joost Batenburg; Robert van Liere (ERCIM News, 2018)

A Data Consistent Variational Segmentation Approach Suitable for Real-time Tomography. - Henri Der Sarkissian; Nicola Viganò; Kees Joost Batenburg (Fundam. Informaticae, 2018)

Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks. - Daniël Maria Pelt; Kees Joost Batenburg; James A. Sethian (J. Imaging, 2018)

A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning. - Henri Der Sarkissian; Felix Lucka; Maureen van Eijnatten; Giulia Colacicco; Sophia Bethany Coban; Kees Joost Batenburg (CoRR, 2019)

A geometric partitioning method for distributed tomographic reconstruction. - Jan-Willem Buurlage; Rob H. Bisseling; Kees Joost Batenburg (Parallel Comput., 2019)

Atomic Super-Resolution Tomography. - Poulami Somanya Ganguly; Felix Lucka; Hermen Jan Hupkes; Kees Joost Batenburg (IWCIA, 2020)

Real-time segmentation for tomographic imaging. - Richard Schoonhoven; Jan-Willem Buurlage; Daniël Maria Pelt; Kees Joost Batenburg (MLSP, 2020)

A projection-based data partitioning method for distributed tomographic reconstruction. - Jan-Willem Buurlage; Rob H. Bisseling; Willem Jan Palenstijn; Kees Joost Batenburg (PP, 2020)

Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging. - Allard A. Hendriksen; Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2020)

Fast ultrasonic imaging using end-to-end deep learning. - Georgios Pilikos; Lars Horchens; Kees Joost Batenburg; Tristan van Leeuwen; Felix Lucka (CoRR, 2020)

Deep data compression for approximate ultrasonic image formation. - Georgios Pilikos; Lars Horchens; Kees Joost Batenburg; Tristan van Leeuwen; Felix Lucka (CoRR, 2020)

LEAN: graph-based pruning for convolutional neural networks by extracting longest chains. - Richard Schoonhoven; Allard A. Hendriksen; Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2020)

CoShaRP: A Convex Program for Single-shot Tomographic Shape Sensing. - Ajinkya Kadu; Tristan van Leeuwen; Kees Joost Batenburg (CoRR, 2020)

Parallel-beam X-ray CT datasets of apples with internal defects and label balancing for machine learning. - Sophia Bethany Coban; Vladyslav Andriiashen; Poulami Somanya Ganguly; Maureen van Eijnatten; Kees Joost Batenburg (CoRR, 2020)

An Efficient Interpolation Approach for Exploring the Parameter Space of Regularized Tomography Algorithms. - Marinus J. Lagerwerf; Willem Jan Palenstijn; Folkert Bleichrodt; Kees Joost Batenburg (Fundam. Informaticae, 2020)

Explorative Imaging and Its Implementation at the FleX-ray Laboratory. - Sophia Bethany Coban; Felix Lucka; Willem Jan Palenstijn; Denis Van Loo; Kees Joost Batenburg (J. Imaging, 2020)

A Computationally Efficient Reconstruction Algorithm for Circular Cone-Beam Computed Tomography Using Shallow Neural Networks. - Marinus J. Lagerwerf; Daniël Maria Pelt; Willem Jan Palenstijn; Kees Joost Batenburg (J. Imaging, 2020)

Emulation of X-ray Light-Field Cameras. - Nicola Viganò; Felix Lucka; Ombeline de La Rochefoucauld; Sophia Bethany Coban; Robert van Liere; Marta Fajardo; Philippe Zeitoun; Kees Joost Batenburg (J. Imaging, 2020)

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. - Mathé T. Zeegers; Daniël Maria Pelt; Tristan van Leeuwen; Robert van Liere; Kees Joost Batenburg (J. Imaging, 2020)

Prototyping X-ray tomographic reconstruction pipelines with FleXbox. - A. Kostenko; Willem Jan Palenstijn; Sophia Bethany Coban; Allard A. Hendriksen; Robert van Liere; Kees Joost Batenburg (SoftwareX, 2020)

Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography. - Allard A. Hendriksen; Daniël Maria Pelt; Kees Joost Batenburg (IEEE Trans. Computational Imaging, 2020)

Automated FDK-Filter Selection for Cone-Beam CT in Research Environments. - Marinus J. Lagerwerf; Willem Jan Palenstijn; Holger Kohr; Kees Joost Batenburg (IEEE Trans. Computational Imaging, 2020)

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning. - Maureen van Eijnatten; Leonardo Rundo; Kees Joost Batenburg; Felix Lucka; Emma Beddowes; Carlos Caldas; Ferdia A. Gallagher; Evis Sala; Carola-Bibiane Schönlieb; Ramona Woitek (Comput. Methods Programs Biomed., 2021)

Comparison of convolutional neural network training strategies for cone-beam CT image segmentation. - Jordi Minnema; Jan Wolff; Juha Koivisto; Felix Lucka; Kees Joost Batenburg; Tymour Forouzanfar; Maureen van Eijnatten (Comput. Methods Programs Biomed., 2021)

Improving reproducibility in synchrotron tomography using implementation-adapted filters. - Poulami Somanya Ganguly; Daniël Maria Pelt; Doga Gürsoy; Francesco De Carlo; Kees Joost Batenburg (CoRR, 2021)

ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography. - Mathé T. Zeegers; Ajinkya Kadu; Tristan van Leeuwen; Kees Joost Batenburg (CoRR, 2021)

Unsupervised Foreign Object Detection Based on Dual-Energy Absorptiometry in the Food Industry. - Vladyslav Andriiashen; Robert van Liere; Tristan van Leeuwen; Kees Joost Batenburg (J. Imaging, 2021)

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications. - Johannes Leuschner; Maximilian Schmidt; Poulami Somanya Ganguly; Vladyslav Andriiashen; Sophia Bethany Coban; Alexander Denker; Dominik F. Bauer; Amir Hadjifaradji; Kees Joost Batenburg; Peter Maass; Maureen van Eijnatten (J. Imaging, 2021)

The Study of Three-Dimensional Fingerprint Recognition in Cultural Heritage: Trends and Challenges. - Dzemila Sero; Isabelle Garachon; Erma Hermens; Robert van Liere; Kees Joost Batenburg (ACM Journal on Computing and Cultural Heritage, 2021)

Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography. - Marinus J. Lagerwerf; Allard A. Hendriksen; Jan-Willem Buurlage; Kees Joost Batenburg (Mach. Learn. Sci. Technol., 2021)

An Analysis of the Impact of Annotation Errors on the Accuracy of Deep Learning for Cell Segmentation. - Serban Vadineanu; Daniël Maria Pelt; Oleh Dzyubachyk; Kees Joost Batenburg (MIDL, 2022)

Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning. - Richard Schoonhoven; Bram Veenboer; Ben van Werkhoven; Kees Joost Batenburg (PMBS@SC, 2022)

SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography. - Poulami Somanya Ganguly; Felix Lucka; Holger Kohr; Erik Franken; Hermen Jan Hupkes; Kees Joost Batenburg (CoRR, 2022)

Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications. - Patrick Echtenbruck; Martina Echtenbruck; Kees Joost Batenburg; Thomas Bäck; Boris Naujoks; Michael Emmerich 001 (CoRR, 2022)

A tomographic workflow to enable deep learning for X-ray based foreign object detection. - Mathé T. Zeegers; Tristan van Leeuwen; Daniël Maria Pelt; Sophia Bethany Coban; Robert van Liere; Kees Joost Batenburg (Expert Syst. Appl., 2022)

SparseAlign: A Grid-Free Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography. - Poulami Somanya Ganguly; Felix Lucka; Holger Kohr; Erik Franken; Hermen Jan Hupkes; Kees Joost Batenburg (IEEE Trans. Computational Imaging, 2022)

TOP-CT: Trajectory With Overlapping Projections X-Ray Computed Tomography. - Dirk Elias Schut; Kees Joost Batenburg; Robert van Liere; Tristan van Leeuwen (IEEE Trans. Computational Imaging, 2022)

SR4ZCT: Self-supervised Through-Plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap. - Jiayang Shi; Daniël Maria Pelt; Kees Joost Batenburg (MLMI@MICCAI (1), 2023)

Real-Time Tilt Undersampling Optimization during Electron Tomography of Beam Sensitive Samples using Golden Ratio Scanning and RECAST3D. - Timothy M. Craig; Ajinkya Kadu; Kees Joost Batenburg; Sara Bals (CoRR, 2023)

Quantifying the effect of X-ray scattering for data generation in real-time defect detection. - Vladyslav Andriiashen; Robert van Liere; Tristan van Leeuwen; Kees Joost Batenburg (CoRR, 2023)

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning. - Maximilian B. Kiss; Sophia Bethany Coban; Kees Joost Batenburg; Tristan van Leeuwen; Felix Lucka (CoRR, 2023)

Multi-stage Deep Learning Artifact Reduction for Computed Tomography. - Jiayang Shi; Daniël Maria Pelt; Kees Joost Batenburg (CoRR, 2023)

Inside out: Fusing 3D imaging modalities for the internal and external investigation of multi-material museum objects. - Francien G. Bossema; Paul J. C. van Laar; Kimberly Meechan; Daniel O'Flynn; Joanne Dyer; Tristan van Leeuwen; Suzan Meijer; Erma Hermens; Kees Joost Batenburg (Digit. Appl. Archaeol. Cult. Heritage, 2023)

Benchmarking Optimization Algorithms for Auto-Tuning GPU Kernels. - Richard Arnoud Schoonhoven; Ben van Werkhoven; Kees Joost Batenburg (IEEE Trans. Evol. Comput., 2023)

From Feature Maps to Few-Shot Cell Segmentation. - Serban Vadineanu; Daniël Maria Pelt; Oleh Dzyubachyk; Kees Joost Batenburg (MOVI@MICCAI, 2024)

Learned denoising with simulated and experimental low-dose CT data. - Maximilian B. Kiss; Ander Biguri; Carola-Bibiane Schönlieb; Kees Joost Batenburg; Felix Lucka (CoRR, 2024)

Benchmarking learned algorithms for computed tomography image reconstruction tasks. - Maximilian B. Kiss; Ander Biguri; Zakhar Shumaylov; Ferdia Sherry; Kees Joost Batenburg; Carola-Bibiane Schönlieb; Felix Lucka (CoRR, 2024)

Single-Shot Tomography of Discrete Dynamic Objects. - Ajinkya Kadu; Felix Lucka; Kees Joost Batenburg (IEEE Trans. Computational Imaging, 2024)

Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction. - Jiayang Shi; Junyi Zhu 002; Daniël Maria Pelt; Kees Joost Batenburg; Matthew B. Blaschko (Trans. Mach. Learn. Res., 2024)

Reducing Manual Annotation Costs for Cell Segmentation by Upgrading Low-Quality Annotations. - Serban Vadineanu; Daniël Maria Pelt; Oleh Dzyubachyk; Kees Joost Batenburg (J. Imaging, 2024)

Agentic Large Language Models, a survey. - Aske Plaat; Max J. van Duijn; Niki van Stein; Mike Preuss; Peter van der Putten; Kees Joost Batenburg (CoRR, 2025)

Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging. - Mathieu Manni; Dmitry Karpov; Kees Joost Batenburg; Sharon Shwartz; Nicola Viganò (CoRR, 2025)