{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/image-reconstruction/papers/19","list_of":"/task/image-reconstruction","task":"Image Reconstruction","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":19,"pages_in_order":22,"rows_per_page":100,"rows":[1801,1900],"of":2143,"counts":{"archive_papers_tagged":2143,"with_a_code_link":712,"where_syntology_ran_a_sample":135,"not_listed_spam_title":0,"listed":2143,"listed_where_code_ran":135,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":114,"every_run_a_failure_of_syntologys_instrument":21,"listed_with_a_run_with_no_instrument_failure":114,"listed_every_run_a_failure_of_syntologys_instrument":21,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/image-reconstruction","prev":"/task/image-reconstruction/papers/18","next":"/task/image-reconstruction/papers/20","papers":[{"url":null,"slug":"rethinking-medical-image-reconstruction-via","title":"Rethinking Medical Image Reconstruction via Shape Prior, Going Deeper and Faster: Deep Joint Indirect Registration and Reconstruction","date":"2019-12-16","arxiv_id":"1912.07648","repositories_listed":0,"syntology":null},{"url":null,"slug":"rodeo-robust-de-aliasing-autoencoder-for-real","title":"RODEO: Robust DE-aliasing autoencOder for Real-time Medical Image Reconstruction","date":"2019-12-11","arxiv_id":"1912.07519","repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramid-convolutional-rnn-for-mri","title":"Pyramid Convolutional RNN for MRI Image Reconstruction","date":"2019-12-02","arxiv_id":"1912.00543","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-similarity-preserving-network-trained-on","title":"A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bat-g-net-bat-inspired-high-resolution-3d","title":"Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes","date":"2019-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"texture-hallucination-for-large-scale","title":"Texture Hallucination for Large-Factor Painting Super-Resolution","date":"2019-12-01","arxiv_id":"1912.00515","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-irregularly-sampled-data-for","title":"Learning from Irregularly Sampled Data for Endomicroscopy Super-resolution: A Comparative Study of Sparse and Dense Approaches","date":"2019-11-29","arxiv_id":"1911.13169","repositories_listed":0,"syntology":null},{"url":null,"slug":"sag-vae-end-to-end-joint-inference-of-data","title":"SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations","date":"2019-11-27","arxiv_id":"1911.11984","repositories_listed":0,"syntology":null},{"url":null,"slug":"2sdr-applying-kronecker-envelope-pca-to","title":"Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy","date":"2019-11-22","arxiv_id":"1911.09816","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-iterative-ct-reconstruction-using","title":"Distributed Iterative CT Reconstruction using Multi-Agent Consensus Equilibrium","date":"2019-11-21","arxiv_id":"1911.09278","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-sampling-reconstruction-theory-and","title":"Optimal Sampling & Reconstruction: Theory and Applications","date":"2019-11-21","arxiv_id":"1911.09595","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-object-segmentation-with","title":"Unsupervised Object Segmentation with Explicit Localization Module","date":"2019-11-21","arxiv_id":"1911.09228","repositories_listed":0,"syntology":null},{"url":null,"slug":"sibling-neural-estimators-improving-iterative","title":"Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication","date":"2019-11-20","arxiv_id":"1911.08478","repositories_listed":0,"syntology":null},{"url":null,"slug":"extra-proximal-gradient-inspired-non-local","title":"Extra Proximal-Gradient Inspired Non-local Network","date":"2019-11-17","arxiv_id":"1911.07144","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-cardiac-cine-mri-beyond","title":"Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction","date":"2019-11-13","arxiv_id":"1911.05845","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-encoder-decoder-adversarial","title":"Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data","date":"2019-11-13","arxiv_id":"1911.05880","repositories_listed":0,"syntology":null},{"url":null,"slug":"recursive-filter-for-space-variant-variance","title":"Recursive Filter for Space-Variant Variance Reduction","date":"2019-11-12","arxiv_id":"1911.04992","repositories_listed":0,"syntology":null},{"url":null,"slug":"limited-view-and-sparse-photoacoustic","title":"Limited View and Sparse Photoacoustic Tomography for Neuroimaging with Deep Learning","date":"2019-11-11","arxiv_id":"1911.04357","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-demosaicing-and-super-resolution-jdsr","title":"Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization","date":"2019-11-08","arxiv_id":"1911.03558","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-domain-cascade-of-u-nets-for-multi","title":"Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction","date":"2019-11-04","arxiv_id":"1911.01458","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-space-variant-deconvolution","title":"Deep Learning for space-variant deconvolution in galaxy surveys","date":"2019-11-01","arxiv_id":"1911.00443","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-learning-a-supervised-unsupervised","title":"SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction","date":"2019-10-26","arxiv_id":"1910.12024","repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-gans-for-mr-imaging-from-paired","title":"Wasserstein GANs for MR Imaging: from Paired to Unpaired Training","date":"2019-10-15","arxiv_id":"1910.07048","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-sparse-gaussian-processes","title":"Regularized Sparse Gaussian Processes","date":"2019-10-13","arxiv_id":"1910.05843","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-detection-and-correction","title":"Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation","date":"2019-10-11","arxiv_id":"1910.05370","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-material-decomposition-with-a-two","title":"Improved Material Decomposition with a Two-step Regularization for spectral CT","date":"2019-10-11","arxiv_id":"1910.05259","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-neural-network-for-multi","title":"Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion","date":"2019-10-09","arxiv_id":"1910.04066","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-sparse-reverse-of-principal-component","title":"The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion","date":"2019-10-04","arxiv_id":"1910.02155","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-within-a-priori-temporal","title":"Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking","date":"2019-10-02","arxiv_id":"1910.00956","repositories_listed":0,"syntology":null},{"url":null,"slug":"ciidefence-defeating-adversarial-attacks-by","title":"CIIDefence: Defeating Adversarial Attacks by Fusing Class-Specific Image Inpainting and Image Denoising","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-hyperspectral-imaging-based-on","title":"Computational Hyperspectral Imaging Based on Dimension-Discriminative Low-Rank Tensor Recovery","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-pet-image-reconstruction-using","title":"Dynamic PET Image Reconstruction Using Nonnegative Matrix Factorization Incorporated With Deep Image Prior","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-reconstruction-using-deep","title":"Hyperspectral Image Reconstruction Using Deep External and Internal Learning","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-expression-recognition-using-1","title":"Facial Expression Recognition Using Disentangled Adversarial Learning","date":"2019-09-28","arxiv_id":"1909.13135","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-have-an-ear-for-face-super","title":"Learning to Have an Ear for Face Super-Resolution","date":"2019-09-27","arxiv_id":"1909.12780","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-consistency-networks-for-calibration","title":"Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction","date":"2019-09-25","arxiv_id":"1909.11795","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-breast-ct-for-radiation","title":"Deep-learning-based Breast CT for Radiation Dose Reduction","date":"2019-09-25","arxiv_id":"1909.11721","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifold-modeling-in-embedded-space-a-1","title":"Manifold Modeling in Embedded Space: A Perspective for Interpreting \"Deep Image Prior\"","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mist-multiple-instance-spatial-transformer-2","title":"MIST: Multiple Instance Spatial Transformer Networks","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pista-sense-resnet-for-parallel-mri","title":"pISTA-SENSE-ResNet for Parallel MRI Reconstruction","date":"2019-09-24","arxiv_id":"1910.00650","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910391","title":"Model-Based and Data-Driven Strategies in Medical Image Computing","date":"2019-09-23","arxiv_id":"1909.10391","repositories_listed":0,"syntology":null},{"url":null,"slug":"infusing-learned-priors-into-model-based","title":"Infusing Learned Priors into Model-Based Multispectral Imaging","date":"2019-09-20","arxiv_id":"1909.09313","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-interpretable-image-synthesis-by","title":"Inducing Hierarchical Compositional Model by Sparsifying Generator Network","date":"2019-09-10","arxiv_id":"1909.04324","repositories_listed":0,"syntology":null},{"url":null,"slug":"faster-and-accurate-classification-for","title":"Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications","date":"2019-09-04","arxiv_id":"1909.05638","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-plug-and-play-prior-for-parallel-mri","title":"Deep Plug-and-Play Prior for Parallel MRI Reconstruction","date":"2019-08-30","arxiv_id":"1909.00089","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-interactive-magnetic-resonance-mr","title":"Real-time interactive magnetic resonance (MR) temperature imaging in both aqueous and adipose tissues using cascaded deep neural networks for MR-guided focused ultrasound surgery (MRgFUS)","date":"2019-08-29","arxiv_id":"1908.10995","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-recurrent-neural-network-for","title":"Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging","date":"2019-08-28","arxiv_id":"1908.10842","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerated-motion-aware-mr-imaging-via","title":"Accelerated Motion-Aware MR Imaging via Motion Prediction from K-Space Center","date":"2019-08-26","arxiv_id":"1908.09560","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-enables-image-reconstruction","title":"Machine-learning enables Image Reconstruction and Classification in a \"see-through\" camera","date":"2019-08-25","arxiv_id":"1908.09401","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807623","title":"Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image","date":"2019-08-20","arxiv_id":"1908.07623","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807516","title":"DirectPET: Full Size Neural Network PET Reconstruction from Sinogram Data","date":"2019-08-19","arxiv_id":"1908.07516","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-automated-image-de-fencing-using","title":"Fully Automated Image De-fencing using Conditional Generative Adversarial Networks","date":"2019-08-19","arxiv_id":"1908.06837","repositories_listed":0,"syntology":null},{"url":null,"slug":"breast-ultrasound-computer-aided-diagnosis","title":"Breast Ultrasound Computer-Aided Diagnosis Using Structure-Aware Triplet Path Networks","date":"2019-08-09","arxiv_id":"1908.09825","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-learning-primal-dual-networks-for-fast","title":"Model Learning: Primal Dual Networks for Fast MR imaging","date":"2019-08-07","arxiv_id":"1908.02426","repositories_listed":0,"syntology":null},{"url":null,"slug":"bcd-net-for-low-dose-ct-reconstruction","title":"BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization","date":"2019-08-04","arxiv_id":"1908.01287","repositories_listed":0,"syntology":null},{"url":null,"slug":"inertial-nonconvex-alternating-minimizations","title":"Inertial nonconvex alternating minimizations for the image deblurring","date":"2019-07-27","arxiv_id":"1907.12945","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-recurrent-plug-and-play-prior-based-on","title":"A New Recurrent Plug-and-Play Prior Based on the Multiple Self-Similarity Network","date":"2019-07-26","arxiv_id":"1907.11793","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressing-deep-quaternion-neural-networks","title":"Compressing deep quaternion neural networks with targeted regularization","date":"2019-07-26","arxiv_id":"1907.11546","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-mri-reconstruction-unrolled-optimization","title":"Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks","date":"2019-07-26","arxiv_id":"1907.11711","repositories_listed":0,"syntology":null},{"url":null,"slug":"momentum-net-fast-and-convergent-iterative","title":"Momentum-Net: Fast and convergent iterative neural network for inverse problems","date":"2019-07-26","arxiv_id":"1907.11818","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-convolutional-forward-and-back-projection","title":"A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry","date":"2019-07-24","arxiv_id":"1907.10526","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-color-holographic","title":"Deep learning-based color holographic microscopy","date":"2019-07-15","arxiv_id":"1907.06727","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-super-resolution-and-nearest","title":"A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data","date":"2019-07-08","arxiv_id":"1907.05283","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-network-for-removing-dct","title":"Fully Convolutional Network for Removing DCT Artefacts From Images","date":"2019-07-08","arxiv_id":"1907.03798","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-with-residual-network-for-single","title":"Distilling with Residual Network for Single Image Super Resolution","date":"2019-07-05","arxiv_id":"1907.02843","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-known-operators-reduces-maximum","title":"Learning with Known Operators reduces Maximum Training Error Bounds","date":"2019-07-03","arxiv_id":"1907.01992","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-image-attribute-editing-using","title":"Semi-supervised Image Attribute Editing using Generative Adversarial Networks","date":"2019-07-03","arxiv_id":"1907.01841","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-network-architecture-for-few-view-ct","title":"Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging","date":"2019-07-02","arxiv_id":"1907.01262","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstructing-perceived-images-from-brain","title":"Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning","date":"2019-06-27","arxiv_id":"1906.12181","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-review-on-deep-learning-in-medical-image","title":"A Review on Deep Learning in Medical Image Reconstruction","date":"2019-06-23","arxiv_id":"1906.10643","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-deep-mr-imaging-the-roadmap-of","title":"Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning","date":"2019-06-19","arxiv_id":"1906.08143","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-variational-networks-with-exponential","title":"Deep Variational Networks with Exponential Weighting for Learning Computed Tomography","date":"2019-06-13","arxiv_id":"1906.05528","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-pet-cardiac-and-parametric-image","title":"Dynamic PET cardiac and parametric image reconstruction: a fixed-point proximity gradient approach using patch-based DCT and tensor SVD regularization","date":"2019-06-13","arxiv_id":"1906.05897","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-methods-for-mr-image","title":"Optimization methods for MR image reconstruction (long version)","date":"2019-06-13","arxiv_id":"1903.03510","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-and-correction-of-cardiac-mr-motion","title":"Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space","date":"2019-06-12","arxiv_id":"1906.05695","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-network-for-1","title":"Generative adversarial network for segmentation of motion affected neonatal brain MRI","date":"2019-06-11","arxiv_id":"1906.04704","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-blind-deblurring-using-multi","title":"Single Image Blind Deblurring Using Multi-Scale Latent Structure Prior","date":"2019-06-11","arxiv_id":"1906.04442","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600882","title":"A new nonlocal forward model for diffuse optical tomography","date":"2019-06-03","arxiv_id":"1906.00882","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600165","title":"Two-layer Residual Sparsifying Transform Learning for Image Reconstruction","date":"2019-06-01","arxiv_id":"1906.00165","repositories_listed":0,"syntology":null},{"url":null,"slug":"cascaded-generative-and-discriminative","title":"Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-reconstruction-using-a","title":"Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"less-memory-faster-speed-refining-self","title":"Less Memory, Faster Speed: Refining Self-Attention Module for Image Reconstruction","date":"2019-05-20","arxiv_id":"1905.08008","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-critical-parameters-of-satellite","title":"Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping","date":"2019-05-17","arxiv_id":"1905.07476","repositories_listed":0,"syntology":null},{"url":null,"slug":"fidelity-imposed-network-edit-fine-for","title":"Fidelity Imposed Network Edit (FINE) for Solving Ill-Posed Image Reconstruction","date":"2019-05-17","arxiv_id":"1905.07284","repositories_listed":0,"syntology":null},{"url":null,"slug":"reconstruction-aware-imaging-system-ranking","title":"Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference","date":"2019-05-14","arxiv_id":"1905.05820","repositories_listed":0,"syntology":null},{"url":null,"slug":"190503658","title":"Improving Discrete Latent Representations With Differentiable Approximation Bridges","date":"2019-05-09","arxiv_id":"1905.03658","repositories_listed":0,"syntology":null},{"url":null,"slug":"dlimd-dictionary-learning-based-image-domain","title":"DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT","date":"2019-05-06","arxiv_id":"1905.02567","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifoldnet-a-deep-neural-network-for","title":"MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-encoder-decoder-architectures-for","title":"Multi-level Encoder-Decoder Architectures for Image Restoration","date":"2019-05-01","arxiv_id":"1905.00322","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifting-autoencoders-unsupervised-learning-of","title":"Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion","date":"2019-04-26","arxiv_id":"1904.11960","repositories_listed":0,"syntology":null},{"url":"/paper/ced-color-event-camera-dataset","slug":"ced-color-event-camera-dataset","title":"CED: Color Event Camera Dataset","date":"2019-04-24","arxiv_id":"1904.10772","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-distributed-fiber-optic-sensing","title":"Computational distributed fiber-optic sensing","date":"2019-04-14","arxiv_id":"1904.06659","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-for-computational","title":"Variational Inference for Computational Imaging Inverse Problems","date":"2019-04-12","arxiv_id":"1904.06264","repositories_listed":0,"syntology":null},{"url":null,"slug":"scanner-invariant-representations-for","title":"Scanner Invariant Representations for Diffusion MRI Harmonization","date":"2019-04-10","arxiv_id":"1904.05375","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-learning-based-ultrasound","title":"End-to-End Learning-Based Ultrasound Reconstruction","date":"2019-04-09","arxiv_id":"1904.04696","repositories_listed":0,"syntology":null},{"url":null,"slug":"controlling-neural-networks-via-energy","title":"Controlling Neural Networks via Energy Dissipation","date":"2019-04-05","arxiv_id":"1904.03081","repositories_listed":0,"syntology":null},{"url":null,"slug":"190502135","title":"Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network","date":"2019-04-04","arxiv_id":"1905.02135","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-reconstruction-from-sparsity-to-data","title":"Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning","date":"2019-04-04","arxiv_id":"1904.02816","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-methods-for-parallel-magnetic","title":"Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction","date":"2019-04-01","arxiv_id":"1904.01112","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-graph-generation-with-external","title":"Scene Graph Generation with External Knowledge and Image Reconstruction","date":"2019-04-01","arxiv_id":"1904.00560","repositories_listed":0,"syntology":null}],"record_sha256":"10a880ac27dd76380c288b71b2c582b74a0bca1f60446632f782649d76565edb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}