{"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/20","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":20,"pages_in_order":22,"rows_per_page":100,"rows":[1901,2000],"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/19","next":"/task/image-reconstruction/papers/21","papers":[{"url":null,"slug":"imaging-cytometry-without-image","title":"Imaging cytometry without image reconstruction (ghost cytometry)","date":"2019-03-27","arxiv_id":"1903.12053","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-pixel-imaging-with-origami-pattern","title":"Single-pixel imaging with origami pattern construction","date":"2019-03-27","arxiv_id":"1903.11432","repositories_listed":0,"syntology":null},{"url":null,"slug":"transform-learning-for-magnetic-resonance","title":"Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks","date":"2019-03-25","arxiv_id":"1903.11431","repositories_listed":0,"syntology":null},{"url":null,"slug":"color-filter-arrays-for-quanta-image-sensors","title":"Color Filter Arrays for Quanta Image Sensors","date":"2019-03-23","arxiv_id":"1903.09823","repositories_listed":0,"syntology":null},{"url":null,"slug":"megapixel-photon-counting-color-imaging-using","title":"Megapixel Photon-Counting Color Imaging using Quanta Image Sensor","date":"2019-03-21","arxiv_id":"1903.09036","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-sparse-coding-for-compressed","title":"Convolutional Sparse Coding for Compressed Sensing CT Reconstruction","date":"2019-03-20","arxiv_id":"1903.08549","repositories_listed":0,"syntology":null},{"url":null,"slug":"plug-and-play-methods-for-magnetic-resonance","title":"Plug and play methods for magnetic resonance imaging (long version)","date":"2019-03-20","arxiv_id":"1903.08616","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-algorithm-for-the-visualization-of","title":"An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves","date":"2019-03-08","arxiv_id":"1903.03254","repositories_listed":0,"syntology":null},{"url":null,"slug":"gated-context-model-with-embedded-priors-for","title":"Gated Context Model with Embedded Priors for Deep Image Compression","date":"2019-02-27","arxiv_id":"1902.10480","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-cell-imaging-in-pet-with-optimal","title":"Dynamic Cell Imaging in PET with Optimal Transport Regularization","date":"2019-02-20","arxiv_id":"1902.07521","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-reconstruction-from-undersampled","title":"Image Reconstruction from Undersampled Confocal Microscopy Data using Multiresolution Based Maximum Entropy Regularization","date":"2019-02-17","arxiv_id":"1902.00061","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-fourier-slice-photography","title":"Local Fourier Slice Photography","date":"2019-02-16","arxiv_id":"1902.06082","repositories_listed":0,"syntology":null},{"url":null,"slug":"breast-cancer-model-reconstruction-and-image","title":"Breast Cancer: Model Reconstruction and Image Registration from Segmented Deformed Image using Visual and Force based Analysis","date":"2019-02-14","arxiv_id":"1902.05340","repositories_listed":0,"syntology":null},{"url":null,"slug":"miso-mutual-information-loss-with-stochastic","title":"MISO: Mutual Information Loss with Stochastic Style Representations for Multimodal Image-to-Image Translation","date":"2019-02-11","arxiv_id":"1902.03938","repositories_listed":0,"syntology":null},{"url":null,"slug":"speeding-up-scaled-gradient-projection","title":"Empirically Accelerating Scaled Gradient Projection Using Deep Neural Network For Inverse Problems In Image Processing","date":"2019-02-07","arxiv_id":"1902.02449","repositories_listed":0,"syntology":null},{"url":null,"slug":"fingerprint-recognition-under-missing-image","title":"Fingerprint Recognition under Missing Image Pixels Scenario","date":"2019-02-06","arxiv_id":"1902.05389","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-x-ray-sparse-view-phase-tomography-via","title":"Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks","date":"2019-01-30","arxiv_id":"1901.10644","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-domain-image-classification-using","title":"Compressed Domain Image Classification Using a Dynamic-Rate Neural Network","date":"2019-01-28","arxiv_id":"1901.09983","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-based-depth-estimation","title":"Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach","date":"2019-01-22","arxiv_id":"1901.07288","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-masked-ct-image-reconstruction-from","title":"Edge-masked CT image reconstruction from limited data","date":"2019-01-16","arxiv_id":"1901.05275","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-waveform-estimation-and","title":"Deep Learning for Waveform Estimation and Imaging in Passive Radar","date":"2019-01-14","arxiv_id":"1809.04768","repositories_listed":0,"syntology":null},{"url":null,"slug":"biomedical-image-reconstruction-from-the","title":"Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks","date":"2019-01-11","arxiv_id":"1901.03565","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-and-finite-element-based-total","title":"Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography","date":"2019-01-07","arxiv_id":"1901.01969","repositories_listed":0,"syntology":null},{"url":null,"slug":"randomized-tensor-ring-decomposition-and-its","title":"Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction","date":"2019-01-07","arxiv_id":"1901.01652","repositories_listed":0,"syntology":null},{"url":null,"slug":"i-can-see-clearly-now-image-restoration-via","title":"I Can See Clearly Now : Image Restoration via De-Raining","date":"2019-01-03","arxiv_id":"1901.00893","repositories_listed":0,"syntology":null},{"url":null,"slug":"off-the-grid-model-based-deep-learning-o-modl","title":"Off-the-grid model based deep learning (O-MODL)","date":"2018-12-27","arxiv_id":"1812.10747","repositories_listed":0,"syntology":null},{"url":null,"slug":"2-5d-deep-learning-for-ct-image","title":"2.5D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation","date":"2018-12-20","arxiv_id":"1812.08367","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-iterative-reconstruction-with","title":"Model Based Iterative Reconstruction With Spatially Adaptive Sinogram Weights for Wide-Cone Cardiac CT","date":"2018-12-20","arxiv_id":"1812.08364","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-beamforming-in-ultrasound-imaging","title":"Learning beamforming in ultrasound imaging","date":"2018-12-19","arxiv_id":"1812.08043","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-loss-for-learning-single-image-based","title":"Hybrid Loss for Learning Single-Image-based HDR Reconstruction","date":"2018-12-18","arxiv_id":"1812.07134","repositories_listed":0,"syntology":null},{"url":null,"slug":"vector-image-generation-by-learning","title":"Unsupervised Image Decomposition in Vector Layers","date":"2018-12-13","arxiv_id":"1812.05484","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesis-of-high-quality-visible-faces-from","title":"Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks","date":"2018-12-12","arxiv_id":"1812.05155","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-variational-model-for-joint-image","title":"A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging","date":"2018-12-09","arxiv_id":"1812.03446","repositories_listed":0,"syntology":null},{"url":null,"slug":"santis-sampling-augmented-neural-network-with","title":"SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction","date":"2018-12-08","arxiv_id":"1812.03278","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-segmentation-with-recurrent","title":"Brain Segmentation from k-space with End-to-end Recurrent Attention Network","date":"2018-12-05","arxiv_id":"1812.02068","repositories_listed":0,"syntology":null},{"url":null,"slug":"jsr-net-a-deep-network-for-joint-spatial","title":"JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data","date":"2018-12-03","arxiv_id":"1812.00510","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-friendly-machine-learning","title":"Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction","date":"2018-12-03","arxiv_id":"1812.01068","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-psychovisual-analysis-on-deep-cnn-features","title":"Why Are Deep Representations Good Perceptual Quality Features?","date":"2018-12-02","arxiv_id":"1812.00412","repositories_listed":0,"syntology":null},{"url":null,"slug":"beltrami-net-domain-independent-deep-d-bar","title":"Beltrami-Net: Domain Independent Deep D-bar Learning for Absolute Imaging with Electrical Impedance Tomography (a-EIT)","date":"2018-11-30","arxiv_id":"1811.12830","repositories_listed":0,"syntology":null},{"url":null,"slug":"undemon-20-improved-depth-and-ego-motion","title":"UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep Image Sampling","date":"2018-11-27","arxiv_id":"1811.10884","repositories_listed":0,"syntology":null},{"url":null,"slug":"automating-motion-correction-in-multishot-mri","title":"Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks","date":"2018-11-24","arxiv_id":"1811.09750","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-adaptive-l2-regularization-image","title":"Edge-adaptive l2 regularization image reconstruction from non-uniform Fourier data","date":"2018-11-20","arxiv_id":"1811.08487","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-synthesize-splitting-and","title":"Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery","date":"2018-11-19","arxiv_id":"1811.07945","repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-and-outlier-resistant-tomographic","title":"Noise- and Outlier-Resistant Tomographic Reconstruction under Unknown Viewing Parameters","date":"2018-11-12","arxiv_id":"1811.04876","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularized-fourier-ptychography-using-an","title":"Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm","date":"2018-10-31","arxiv_id":"1811.00120","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-using-total-variation","title":"Image Restoration using Total Variation Regularized Deep Image Prior","date":"2018-10-30","arxiv_id":"1810.12864","repositories_listed":0,"syntology":null},{"url":null,"slug":"audiovisual-speaker-conversion-jointly-and","title":"Audiovisual speaker conversion: jointly and simultaneously transforming facial expression and acoustic characteristics","date":"2018-10-29","arxiv_id":"1810.12730","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-plus-motion-csm-a-new","title":"Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction","date":"2018-10-25","arxiv_id":"1810.10828","repositories_listed":0,"syntology":null},{"url":null,"slug":"humans-are-still-the-best-lossy-image","title":"Towards improved lossy image compression: Human image reconstruction with public-domain images","date":"2018-10-25","arxiv_id":"1810.11137","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-super-resolution-in","title":"Deep learning-based super-resolution in coherent imaging systems","date":"2018-10-15","arxiv_id":"1810.06611","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-bi-modal-image-reconstruction-of-dot","title":"Joint bi-modal image reconstruction of DOT and XCT with an extended Mumford-Shah functional","date":"2018-10-15","arxiv_id":"1810.06203","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-ghost-imaging-using-a-field","title":"Computational ghost imaging using a field-programmable gate array","date":"2018-10-10","arxiv_id":"1810.05670","repositories_listed":0,"syntology":null},{"url":null,"slug":"computationally-efficient-deep-neural-network","title":"Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction","date":"2018-10-05","arxiv_id":"1810.03999","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimension-dynamic-mr-imaging-with-both-k","title":"DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training","date":"2018-09-30","arxiv_id":"1810.00302","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-wise-and-spatial-feature-modulation","title":"Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution","date":"2018-09-28","arxiv_id":"1809.11130","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-reconstruction-using-deep-learning","title":"Image Reconstruction Using Deep Learning","date":"2018-09-27","arxiv_id":"1809.10410","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-adaptive-image-reconstruction-onair","title":"Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models","date":"2018-09-06","arxiv_id":"1809.01817","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-of-deep-learning-for-magnetic","title":"Geometry of Deep Learning for Magnetic Resonance Fingerprinting","date":"2018-09-05","arxiv_id":"1809.01749","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-computing-for-fibre-bundle","title":"Image computing for fibre-bundle endomicroscopy: A review","date":"2018-09-03","arxiv_id":"1809.00604","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-adapted-reconstruction-for-inverse","title":"Task adapted reconstruction for inverse problems","date":"2018-08-27","arxiv_id":"1809.00948","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-graphical-modeling-approach-to","title":"Probabilistic Graphical Modeling approach to dynamic PET direct parametric map estimation and image reconstruction","date":"2018-08-24","arxiv_id":"1808.08286","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-boosted-regression-for-mr-to-ct","title":"Deep Boosted Regression for MR to CT Synthesis","date":"2018-08-22","arxiv_id":"1808.07431","repositories_listed":0,"syntology":null},{"url":null,"slug":"radon-inversion-via-deep-learning","title":"Radon Inversion via Deep Learning","date":"2018-08-09","arxiv_id":"1808.03015","repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-accelerated-multishot-epi-through","title":"Highly Accelerated Multishot EPI through Synergistic Machine Learning and Joint Reconstruction","date":"2018-08-08","arxiv_id":"1808.02814","repositories_listed":0,"syntology":null},{"url":null,"slug":"semblance-a-rank-based-kernel-on-probability","title":"Semblance: A Rank-Based Kernel on Probability Spaces for Niche Detection","date":"2018-08-06","arxiv_id":"1808.02061","repositories_listed":0,"syntology":null},{"url":null,"slug":"x-gans-image-reconstruction-made-easy-for","title":"X-GANs: Image Reconstruction Made Easy for Extreme Cases","date":"2018-08-06","arxiv_id":"1808.04432","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-complementary-regularizers-image","title":"The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling","date":"2018-08-03","arxiv_id":"1808.01316","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesizing-ct-from-ultrashort-echo-time-mr","title":"Synthesizing CT from Ultrashort Echo-Time MR Images via Convolutional Neural Networks","date":"2018-07-27","arxiv_id":"1807.10850","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-reconstruction-via-variational-network","title":"Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging","date":"2018-07-19","arxiv_id":"1807.07416","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-physical-preprocessing-for-example","title":"Optimal Physical Preprocessing for Example-Based Super-Resolution","date":"2018-07-12","arxiv_id":"1807.04813","repositories_listed":0,"syntology":null},{"url":null,"slug":"complex-fully-convolutional-neural-networks","title":"Complex Fully Convolutional Neural Networks for MR Image Reconstruction","date":"2018-07-09","arxiv_id":"1807.03343","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-back-projection-for-sparse-view-ct","title":"Deep Back Projection for Sparse-View CT Reconstruction","date":"2018-07-06","arxiv_id":"1807.02370","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-personalized-representation-for","title":"Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network","date":"2018-07-04","arxiv_id":"1807.01759","repositories_listed":0,"syntology":null},{"url":null,"slug":"stability-of-scattering-decoder-for-nonlinear","title":"Stability of Scattering Decoder For Nonlinear Diffractive Imaging","date":"2018-06-20","arxiv_id":"1806.08015","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-decode-7t-like-mr-image","title":"Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images","date":"2018-06-18","arxiv_id":"1806.06886","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-reconstruction-through-metamorphosis","title":"Image reconstruction through metamorphosis","date":"2018-06-04","arxiv_id":"1806.01225","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-a-thinking-microscope-deep-learning-in","title":"Toward a Thinking Microscope: Deep Learning in Optical Microscopy and Image Reconstruction","date":"2018-05-23","arxiv_id":"1805.08970","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-based-mixed-noise-removal-with","title":"Variational based Mixed Noise Removal with CNN Deep Learning Regularization","date":"2018-05-21","arxiv_id":"1805.08094","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-object-classification-in-single-pixel","title":"Fast Object Classification in Single-pixel Imaging","date":"2018-05-19","arxiv_id":"1805.07582","repositories_listed":0,"syntology":null},{"url":null,"slug":"photorealistic-image-reconstruction-from","title":"Photorealistic Image Reconstruction from Hybrid Intensity and Event based Sensor","date":"2018-05-16","arxiv_id":"1805.06140","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-cs-mri-reconstruction-and-segmentation","title":"Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network","date":"2018-05-06","arxiv_id":"1805.02165","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeppet-a-deep-encoder-decoder-network-for","title":"DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problem","date":"2018-04-20","arxiv_id":"1804.07851","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-convolutional-sparse-coding","title":"Supervised Convolutional Sparse Coding","date":"2018-04-08","arxiv_id":"1804.02678","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-ultrasound-image-reconstruction","title":"Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning","date":"2018-04-06","arxiv_id":"1804.02119","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-axially-variant-kernel-imaging-model","title":"An axially-variant kernel imaging model applied to ultrasound image reconstruction","date":"2018-03-27","arxiv_id":"1801.08479","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-optimization-of-high-capacity","title":"Generalized Optimization of High Capacity Compressive Imaging Systems","date":"2018-03-22","arxiv_id":"1803.08184","repositories_listed":0,"syntology":null},{"url":null,"slug":"extended-depth-of-field-in-holographic-image","title":"Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery","date":"2018-03-21","arxiv_id":"1803.08138","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlocal-low-rank-tensor-factor-analysis-for","title":"Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration","date":"2018-03-19","arxiv_id":"1803.06795","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-relaxed-two-dimensional-color","title":"Sample-Relaxed Two-Dimensional Color Principal Component Analysis for Face Recognition and Image Reconstruction","date":"2018-03-10","arxiv_id":"1803.03837","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-based-sinogram-synthesis","title":"Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction","date":"2018-03-02","arxiv_id":"1803.00694","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-photoacoustic-image","title":"Model-Based Photoacoustic Image Reconstruction using Compressed Sensing and Smoothed L0 Norm","date":"2018-02-26","arxiv_id":"1802.09313","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-dimensional-photoacoustic-tomography","title":"Three-Dimensional Photoacoustic Tomography using Delay Multiply and Sum Beamforming Algorithm","date":"2018-02-26","arxiv_id":"1802.09310","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-super-resolution-via-cascaded","title":"Single Image Super-Resolution via Cascaded Multi-Scale Cross Network","date":"2018-02-24","arxiv_id":"1802.08808","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-image-reconstruction-via","title":"Learning-based Image Reconstruction via Parallel Proximal Algorithm","date":"2018-01-29","arxiv_id":"1801.09518","repositories_listed":0,"syntology":null},{"url":null,"slug":"meshed-up-learnt-error-correction-in-3d","title":"Meshed Up: Learnt Error Correction in 3D Reconstructions","date":"2018-01-27","arxiv_id":"1801.09128","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraint-free-natural-image-reconstruction","title":"Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network","date":"2018-01-16","arxiv_id":"1801.05151","repositories_listed":0,"syntology":null},{"url":null,"slug":"cortical-inspired-image-reconstruction-via","title":"Cortical-inspired image reconstruction via sub-Riemannian geometry and hypoelliptic diffusion","date":"2018-01-11","arxiv_id":"1801.03800","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovery-of-point-clouds-on-surfaces","title":"Recovery of Point Clouds on Surfaces: Application to Image Reconstruction","date":"2018-01-03","arxiv_id":"1801.00886","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-of-image-gradients-and-total","title":"Denoising of image gradients and total generalized variation denoising","date":"2017-12-22","arxiv_id":"1712.08585","repositories_listed":0,"syntology":null},{"url":null,"slug":"attenuation-correction-for-brain-pet-imaging","title":"Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images","date":"2017-12-17","arxiv_id":"1712.06203","repositories_listed":0,"syntology":null}],"record_sha256":"b61d216daebcc9f7061628e53cc432eaa15629c80dcc2bf3f284d5523356a11c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}