{"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/super-resolution/papers/35","list_of":"/task/super-resolution","task":"Super-Resolution","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":35,"pages_in_order":39,"rows_per_page":100,"rows":[3401,3500],"of":3874,"counts":{"archive_papers_tagged":3874,"with_a_code_link":1627,"where_syntology_ran_a_sample":382,"not_listed_spam_title":0,"listed":3874,"listed_where_code_ran":382,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":343,"every_run_a_failure_of_syntologys_instrument":39,"listed_with_a_run_with_no_instrument_failure":343,"listed_every_run_a_failure_of_syntologys_instrument":39,"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/super-resolution","prev":"/task/super-resolution/papers/34","next":"/task/super-resolution/papers/36","papers":[{"url":null,"slug":"super-resolved-localisation-without","title":"Super-resolved Localisation without Identifying LoS/NLoS Paths","date":"2019-11-20","arxiv_id":"1910.12662","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-neural-architecture-search","title":"Fine-Grained Neural Architecture Search","date":"2019-11-18","arxiv_id":"1911.07478","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-deep-guided-filtering-for","title":"Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing","date":"2019-11-18","arxiv_id":"1911.07731","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-automation-of-deep-image-prior","title":"Towards the Automation of Deep Image Prior","date":"2019-11-17","arxiv_id":"1911.07185","repositories_listed":0,"syntology":null},{"url":null,"slug":"longitudinal-analysis-of-fetal-mri-in","title":"Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair","date":"2019-11-15","arxiv_id":"1911.06542","repositories_listed":0,"syntology":null},{"url":null,"slug":"neutron-ghost-imaging","title":"Neutron Ghost Imaging","date":"2019-11-13","arxiv_id":"1911.06145","repositories_listed":0,"syntology":null},{"url":null,"slug":"degrees-of-freedom-for-off-the-grid-sparse","title":"Degrees of freedom for off-the-grid sparse estimation","date":"2019-11-08","arxiv_id":"1911.03577","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-super-resolution-via-residual-blended","title":"Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks","date":"2019-11-08","arxiv_id":"1911.03464","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":"cross-scale-residual-network-for-multiple","title":"Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking","date":"2019-11-04","arxiv_id":"1911.01257","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-super-resolution-of-solar","title":"Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties","date":"2019-11-04","arxiv_id":"1911.01486","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-frame-super-resolution-of-solar","title":"Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \\& Losses","date":"2019-11-04","arxiv_id":"1911.01490","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-set-affect-on-super-resolution-for","title":"Training Set Effect on Super Resolution for Automated Target Recognition","date":"2019-10-29","arxiv_id":"1911.07934","repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-expression-restoration-based-on","title":"Facial Expression Restoration Based on Improved Graph Convolutional Networks","date":"2019-10-23","arxiv_id":"1910.10344","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-super-resolution-a-coupled-1","title":"Hyperspectral Super-resolution: A Coupled Nonnegative Block-term Tensor Decomposition Approach","date":"2019-10-22","arxiv_id":"1910.10275","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-deconvolution-with-deep-image-and","title":"Image Deconvolution with Deep Image and Kernel Priors","date":"2019-10-18","arxiv_id":"1910.08386","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-image-super-resolution-via-deep","title":"Multimodal Image Super-resolution via Deep Unfolding with Side Information","date":"2019-10-18","arxiv_id":"1910.08320","repositories_listed":0,"syntology":null},{"url":null,"slug":"translation-position-extracting-in-incoherent","title":"Translation position extracting in incoherent Fourier ptychography","date":"2019-10-17","arxiv_id":"1910.08397","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnn-a-block-based-and-highly-parallel-cnn","title":"eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference","date":"2019-10-13","arxiv_id":"1910.05680","repositories_listed":0,"syntology":null},{"url":null,"slug":"ernet-family-hardware-oriented-cnn-models-for","title":"ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference","date":"2019-10-13","arxiv_id":"1910.05787","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":"unsupervised-image-super-resolution-with-an","title":"Unsupervised Image Super-Resolution with an Indirect Supervised Path","date":"2019-10-07","arxiv_id":"1910.02593","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-to-follow-follow-to-be-better-towards","title":"Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-blind-hyperspectral-image-fusion","title":"Deep Blind Hyperspectral Image Fusion","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-at-scale-for-subgrid-modeling","title":"Deep learning at scale for subgrid modeling in turbulent flows","date":"2019-10-01","arxiv_id":"1910.00928","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedded-block-residual-network-a-recursive","title":"Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution","date":"2019-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"coarse-to-fine-registration-of-airborne-lidar","title":"Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes","date":"2019-09-30","arxiv_id":"1909.13817","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-photoacoustic-and-ultrasound","title":"Super-resolution photoacoustic and ultrasound imaging with sparse arrays","date":"2019-09-30","arxiv_id":"1910.00390","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-projection-networks-for","title":"Unsupervised Projection Networks for Generative Adversarial Networks","date":"2019-09-30","arxiv_id":"1910.00579","repositories_listed":0,"syntology":null},{"url":null,"slug":"frame-and-feature-context-video-super","title":"Frame and Feature-Context Video Super-Resolution","date":"2019-09-28","arxiv_id":"1909.13057","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":"multi-grained-attention-networks-for-single","title":"Multi-grained Attention Networks for Single Image Super-Resolution","date":"2019-09-26","arxiv_id":"1909.11937","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-interpretation-of-deep-cnn","title":"Analysis and Interpretation of Deep CNN Representations as Perceptual Quality Features","date":"2019-09-25","arxiv_id":null,"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":"optimal-transport-cyclegan-and-penalized-ls","title":"Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems","date":"2019-09-25","arxiv_id":"1909.12116","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixel-co-occurence-based-loss-metrics-for","title":"Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"relative-pixel-prediction-for-autoregressive","title":"Relative Pixel Prediction For Autoregressive Image Generation","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-traffic-scene-predictions-with","title":"Enhancing Traffic Scene Predictions with Generative Adversarial Networks","date":"2019-09-24","arxiv_id":"1909.10833","repositories_listed":0,"syntology":null},{"url":null,"slug":"190910136","title":"DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme","date":"2019-09-23","arxiv_id":"1909.10136","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-learning-for-real-world-super","title":"Unsupervised Learning for Real-World Super-Resolution","date":"2019-09-20","arxiv_id":"1909.09629","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeply-matting-based-dual-generative","title":"Deeply Matting-based Dual Generative Adversarial Network for Image and Document Label Supervision","date":"2019-09-19","arxiv_id":"1909.12909","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-fan-multi-spectral-mosaic-super","title":"Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network","date":"2019-09-17","arxiv_id":"1909.07577","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-low-field-to-high-field-mr","title":"Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator","date":"2019-09-15","arxiv_id":"1909.06763","repositories_listed":0,"syntology":null},{"url":null,"slug":"phase-retrieval-using-untrained-neural","title":"Phase Retrieval using Untrained Neural Network Priors","date":"2019-09-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-mr-brain-image-super-resolution-using","title":"Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors","date":"2019-09-10","arxiv_id":"1909.04572","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-super-resolution-network-for-single","title":"Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations","date":"2019-09-09","arxiv_id":"1909.03748","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-learning-based-super-resolution","title":"Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation","date":"2019-09-06","arxiv_id":"1909.02825","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-online-video-super-resolution-using-an","title":"Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme","date":"2019-08-30","arxiv_id":"1909.00073","repositories_listed":0,"syntology":null},{"url":null,"slug":"virtual-thin-slice-3d-conditional-gan-based","title":"Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval","date":"2019-08-30","arxiv_id":"1908.11506","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":"robust-regression-via-deep-negative","title":"Robust Regression via Deep Negative Correlation Learning","date":"2019-08-24","arxiv_id":"1908.09066","repositories_listed":0,"syntology":null},{"url":null,"slug":"drfn-deep-recurrent-fusion-network-for-single","title":"DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution with Large Factors","date":"2019-08-23","arxiv_id":"1908.08837","repositories_listed":0,"syntology":null},{"url":null,"slug":"190807985","title":"MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors","date":"2019-08-21","arxiv_id":"1908.07985","repositories_listed":0,"syntology":null},{"url":null,"slug":"srobb-targeted-perceptual-loss-for-single","title":"SROBB: Targeted Perceptual Loss for Single Image Super-Resolution","date":"2019-08-20","arxiv_id":"1908.07222","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-slice-interpolation-via-marginal-super","title":"Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement","date":"2019-08-15","arxiv_id":"1908.05599","repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-aligning-millions-of-images-with-deep","title":"Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing","date":"2019-08-12","arxiv_id":"1908.04130","repositories_listed":0,"syntology":null},{"url":null,"slug":"linear-depthwise-convolution-for-single-image","title":"Attention-Aware Linear Depthwise Convolution for Single Image Super-Resolution","date":"2019-08-07","arxiv_id":"1908.02648","repositories_listed":0,"syntology":null},{"url":null,"slug":"architecture-aware-network-pruning-for-vision","title":"Architecture-aware Network Pruning for Vision Quality Applications","date":"2019-08-05","arxiv_id":"1908.02125","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-contrast-super-resolution-mri-through-a","title":"Multi-Contrast Super-Resolution MRI Through a Progressive Network","date":"2019-08-05","arxiv_id":"1908.01612","repositories_listed":0,"syntology":null},{"url":null,"slug":"crnet-image-super-resolution-using-a","title":"CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network","date":"2019-08-03","arxiv_id":"1908.01166","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-motion-modeling-from-medical","title":"Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRI","date":"2019-07-31","arxiv_id":"1907.13524","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-quantification-in-deep-learning","title":"Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement","date":"2019-07-31","arxiv_id":"1907.13418","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-there-any-recovery-guarantee-with-coupled","title":"Is There Any Recovery Guarantee with Coupled Structured Matrix Factorization for Hyperspectral Super-Resolution?","date":"2019-07-30","arxiv_id":"1907.12728","repositories_listed":0,"syntology":null},{"url":null,"slug":"benefiting-from-multitask-learning-to-improve","title":"Benefiting from Multitask Learning to Improve Single Image Super-Resolution","date":"2019-07-29","arxiv_id":"1907.12488","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-enhancement-by-recurrently-trained","title":"Image Enhancement by Recurrently-trained Super-resolution Network","date":"2019-07-26","arxiv_id":"1907.11341","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-super-resolution-convolution-neural","title":"Improved Super-Resolution Convolution Neural Network for Large Images","date":"2019-07-26","arxiv_id":"1907.12928","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-resolution-invariant-deep","title":"Learning Resolution-Invariant Deep Representations for Person Re-Identification","date":"2019-07-25","arxiv_id":"1907.10843","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neural-lens-for-super-resolution-biological","title":"A neural lens for super-resolution biological imaging","date":"2019-07-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-channel-estimation-for","title":"Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems","date":"2019-07-16","arxiv_id":"1907.07206","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-resolution-and-recovering-texture-of","title":"Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning","date":"2019-07-15","arxiv_id":"1907.07131","repositories_listed":0,"syntology":null},{"url":null,"slug":"coupled-projection-residual-network-for-mri","title":"Coupled-Projection Residual Network for MRI Super-Resolution","date":"2019-07-12","arxiv_id":"1907.05598","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-generative-adversarial-network-for","title":"Enhanced generative adversarial network for 3D brain MRI super-resolution","date":"2019-07-10","arxiv_id":"1907.04835","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-meets-machine-learning","title":"Super-resolution meets machine learning: approximation of measures","date":"2019-07-10","arxiv_id":"1907.04895","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":"improving-the-resolution-of-microscope-by","title":"Improving the resolution of microscope by deconvolution after dense scan","date":"2019-07-06","arxiv_id":"1907.05275","repositories_listed":0,"syntology":null},{"url":null,"slug":"mri-super-resolution-with-ensemble-learning","title":"MRI Super-Resolution with Ensemble Learning and Complementary Priors","date":"2019-07-06","arxiv_id":"1907.03063","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-in-ultrasound-imaging","title":"Deep learning in ultrasound imaging","date":"2019-07-05","arxiv_id":"1907.02994","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":"image-super-resolution-using-attention-based","title":"Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution","date":"2019-07-03","arxiv_id":"1907.05282","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-of-proba-v-images-using","title":"Super-Resolution of PROBA-V Images Using Convolutional Neural Networks","date":"2019-07-03","arxiv_id":"1907.01821","repositories_listed":0,"syntology":null},{"url":null,"slug":"tranquil-clouds-neural-networks-for-learning","title":"Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds","date":"2019-07-03","arxiv_id":"1907.05279","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-single-video-super-resolution-gan-for","title":"A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models","date":"2019-07-02","arxiv_id":"1907.01399","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-based-synthesis-of-realistic-high","title":"CNN-based synthesis of realistic high-resolution LiDAR data","date":"2019-06-28","arxiv_id":"1907.00787","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cnn-based-super-resolution-technique-for","title":"A CNN-Based Super-Resolution Technique for Active Fire Detection on Sentinel-2 Data","date":"2019-06-25","arxiv_id":"1906.10413","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-super-resolution-via-bilinear-pooling","title":"Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy","date":"2019-06-18","arxiv_id":"1906.07802","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-training-deep-networks-for-satellite-image","title":"On training deep networks for satellite image super-resolution","date":"2019-06-16","arxiv_id":"1906.06697","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-super-resolution-via-dense","title":"Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis","date":"2019-06-15","arxiv_id":"1906.06575","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-image-noise-modeling-with-self","title":"Unsupervised Image Noise Modeling with Self-Consistent GAN","date":"2019-06-13","arxiv_id":"1906.05762","repositories_listed":0,"syntology":null},{"url":null,"slug":"190604809","title":"Suppressing Model Overfitting for Image Super-Resolution Networks","date":"2019-06-11","arxiv_id":"1906.04809","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-between-adversarial","title":"A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression","date":"2019-06-11","arxiv_id":"1906.04681","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressed-sensing-mri-via-a-multi-scale","title":"Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution Network","date":"2019-06-11","arxiv_id":"1906.05251","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":"convolutional-bipartite-attractor-networks","title":"Convolutional Bipartite Attractor Networks","date":"2019-06-08","arxiv_id":"1906.03504","repositories_listed":0,"syntology":null},{"url":null,"slug":"190600254","title":"Super-resolution of Time-series Labels for Bootstrapped Event Detection","date":"2019-06-01","arxiv_id":"1906.00254","repositories_listed":0,"syntology":null},{"url":null,"slug":"ode-inspired-network-design-for-single-image","title":"ODE-Inspired Network Design for Single Image Super-Resolution","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"residual-networks-for-light-field-image-super","title":"Residual Networks for Light Field Image Super-Resolution","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zoom-to-learn-learn-to-zoom-1","title":"Zoom to Learn, Learn to Zoom","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"190513300","title":"Generative Imaging and Image Processing via Generative Encoder","date":"2019-05-23","arxiv_id":"1905.13300","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-quality-assessment-for-determining","title":"Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)","date":"2019-05-14","arxiv_id":"1905.05373","repositories_listed":0,"syntology":null}],"record_sha256":"fc6cc486282c0a9d6b36badcdb0f9b540537b404df9f428fee742eb3f76481f0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}