{"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-super-resolution/papers/13","list_of":"/task/image-super-resolution","task":"Image 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":13,"pages_in_order":16,"rows_per_page":100,"rows":[1201,1300],"of":1589,"counts":{"archive_papers_tagged":1589,"with_a_code_link":783,"where_syntology_ran_a_sample":188,"not_listed_spam_title":0,"listed":1589,"listed_where_code_ran":188,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":171,"every_run_a_failure_of_syntologys_instrument":17,"listed_with_a_run_with_no_instrument_failure":171,"listed_every_run_a_failure_of_syntologys_instrument":17,"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-super-resolution","prev":"/task/image-super-resolution/papers/12","next":"/task/image-super-resolution/papers/14","papers":[{"url":null,"slug":"image-super-resolution-using-t-tetromino","title":"Image Super-Resolution Using T-Tetromino Pixels","date":"2021-11-17","arxiv_id":"2111.09013","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-latent-encoder-coupled-generative","title":"A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution","date":"2021-11-16","arxiv_id":"2111.08685","repositories_listed":0,"syntology":null},{"url":null,"slug":"gdca-gan-based-single-image-super-resolution","title":"GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention","date":"2021-11-09","arxiv_id":"2111.05014","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-aware-physics-inspired-degradation","title":"Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution","date":"2021-11-05","arxiv_id":"2111.03301","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-spectral-multi-image-super-resolution","title":"Multi-Spectral Multi-Image Super-Resolution of Sentinel-2 with Radiometric Consistency Losses and Its Effect on Building Delineation","date":"2021-11-05","arxiv_id":"2111.03231","repositories_listed":0,"syntology":null},{"url":null,"slug":"remote-sensing-image-super-resolution-and","title":"Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art","date":"2021-11-05","arxiv_id":"2111.03260","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-dual-attention-network-for-light-field","title":"Dense Dual-Attention Network for Light Field Image Super-Resolution","date":"2021-10-23","arxiv_id":"2110.12114","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectrum-to-kernel-translation-for-accurate","title":"Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution","date":"2021-10-23","arxiv_id":"2110.12151","repositories_listed":0,"syntology":null},{"url":null,"slug":"error-correcting-neural-networks-for-semi","title":"Error-correcting neural networks for semi-Lagrangian advection in the level-set method","date":"2021-10-22","arxiv_id":"2110.11611","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-inspired-autoencoder-for-unsupervised","title":"Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution","date":"2021-10-22","arxiv_id":"2110.11591","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-boost-multimodal-medical-image","title":"Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform","date":"2021-10-22","arxiv_id":"2110.11684","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-real-world-image-super-resolution-via","title":"Toward Real-world Image Super-resolution via Hardware-based Adaptive Degradation Models","date":"2021-10-20","arxiv_id":"2110.10755","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-orbit-lunar-satellite-image-super","title":"In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission","date":"2021-10-19","arxiv_id":"2110.10109","repositories_listed":0,"syntology":null},{"url":"/paper/locally-adaptive-structure-and-texture","slug":"locally-adaptive-structure-and-texture","title":"Locally Adaptive Structure and Texture Similarity for Image Quality Assessment","date":"2021-10-16","arxiv_id":"2110.08521","repositories_listed":0,"syntology":null},{"url":null,"slug":"paradis-parallelly-distributable-slimmable","title":"ParaDiS: Parallelly Distributable Slimmable Neural Networks","date":"2021-10-06","arxiv_id":"2110.02724","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-investigation-of-pre-upsampling-generative","title":"An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution","date":"2021-09-30","arxiv_id":"2109.14994","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-efficient-image-super-resolution","title":"Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"resolution-based-feature-distillation-for","title":"Resolution based Feature Distillation for Cross Resolution Person Re-Identification","date":"2021-09-16","arxiv_id":"2109.07871","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-real-world-super-resolution-via","title":"Toward Real-World Super-Resolution via Adaptive Downsampling Models","date":"2021-09-08","arxiv_id":"2109.03444","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusformer-a-transformer-based-fusion-approach","title":"Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution","date":"2021-09-05","arxiv_id":"2109.02079","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-approach-for-hyperspectral","title":"Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction","date":"2021-09-03","arxiv_id":"2109.01403","repositories_listed":0,"syntology":null},{"url":null,"slug":"infrared-image-super-resolution-via","title":"Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN","date":"2021-09-02","arxiv_id":"2109.00960","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-splitting-and-aggregation-network","title":"From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution","date":"2021-08-31","arxiv_id":"2108.13584","repositories_listed":0,"syntology":null},{"url":"/paper/multi-attributed-and-structured-text-to-face","slug":"multi-attributed-and-structured-text-to-face","title":"Multi-Attributed and Structured Text-to-Face Synthesis","date":"2021-08-25","arxiv_id":"2108.11100","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-on-mobile-real-time-super","title":"Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search","date":"2021-08-18","arxiv_id":"2108.08910","repositories_listed":0,"syntology":null},{"url":null,"slug":"fa-gan-fused-attentive-generative-adversarial","title":"FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution","date":"2021-08-09","arxiv_id":"2108.03920","repositories_listed":0,"syntology":null},{"url":null,"slug":"fl-misr-fast-large-scale-multi-image-super","title":"FL-MISR: Fast Large-Scale Multi-Image Super-Resolution for Computed Tomography Based on Multi-GPU Acceleration","date":"2021-08-09","arxiv_id":"2108.04315","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-reference-training-data-acquisition-and","title":"Data Acquisition and Preparation for Dual-reference Deep Learning of Image Super-Resolution","date":"2021-08-05","arxiv_id":"2108.02348","repositories_listed":0,"syntology":null},{"url":null,"slug":"mfagan-a-compression-framework-for-memory","title":"MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN","date":"2021-07-27","arxiv_id":"2107.12679","repositories_listed":0,"syntology":null},{"url":null,"slug":"laconv-local-adaptive-convolution-for-image","title":"LAConv: Local Adaptive Convolution for Image Fusion","date":"2021-07-24","arxiv_id":"2107.11617","repositories_listed":0,"syntology":null},{"url":null,"slug":"ranksrgan-super-resolution-generative","title":"RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank","date":"2021-07-20","arxiv_id":"2107.09427","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-attention-generative-adversarial","title":"Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution","date":"2021-07-14","arxiv_id":"2107.06536","repositories_listed":0,"syntology":null},{"url":null,"slug":"effectiveness-of-state-of-the-art-super","title":"Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment","date":"2021-07-08","arxiv_id":"2107.04133","repositories_listed":0,"syntology":null},{"url":null,"slug":"regional-differential-information-entropy-for","title":"Image restoration quality assessment based on regional differential information entropy","date":"2021-07-08","arxiv_id":"2107.03642","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-residual-star-generative-adversarial","title":"A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution","date":"2021-07-07","arxiv_id":"2107.03145","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-image-super-resolution-a-survey-and","title":"Blind Image Super-Resolution: A Survey and Beyond","date":"2021-07-07","arxiv_id":"2107.03055","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-deep-learning-based-image-super","title":"Impact of deep learning-based image super-resolution on binary signal detection","date":"2021-07-06","arxiv_id":"2107.02338","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-image-super-resolution-via-contrastive","title":"Blind Image Super-Resolution via Contrastive Representation Learning","date":"2021-07-01","arxiv_id":"2107.00708","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-free-knowledge-distillation-for-image","title":"Data-Free Knowledge Distillation for Image Super-Resolution","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lau-net-latitude-adaptive-upscaling-network","title":"LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/mr-image-super-resolution-with-squeeze-and","slug":"mr-image-super-resolution-with-squeeze-and","title":"MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"protecting-intellectual-property-of-1","title":"Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multi-scale-backbone-with","title":"Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution","date":"2021-06-18","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feedback-pyramid-attention-networks-for","title":"Feedback Pyramid Attention Networks for Single Image Super-Resolution","date":"2021-06-13","arxiv_id":"2106.06966","repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramidal-dense-attention-networks-for","title":"Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution","date":"2021-06-13","arxiv_id":"2106.06996","repositories_listed":0,"syntology":null},{"url":null,"slug":"soup-gan-super-resolution-mri-using","title":"SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks","date":"2021-06-04","arxiv_id":"2106.02599","repositories_listed":0,"syntology":null},{"url":null,"slug":"bilateral-spectrum-weighted-total-variation","title":"Bilateral Spectrum Weighted Total Variation for Noisy-Image Super-Resolution and Image Denoising","date":"2021-06-01","arxiv_id":"2106.00768","repositories_listed":0,"syntology":null},{"url":null,"slug":"fourier-space-losses-for-efficient-perceptual","title":"Fourier Space Losses for Efficient Perceptual Image Super-Resolution","date":"2021-06-01","arxiv_id":"2106.00783","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-organized-residual-blocks-for-image","title":"Self-Organized Residual Blocks for Image Super-Resolution","date":"2021-05-31","arxiv_id":"2105.14926","repositories_listed":0,"syntology":null},{"url":null,"slug":"blind-motion-deblurring-super-resolution-when","title":"Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding","date":"2021-05-27","arxiv_id":"2105.13077","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-frequency-aware-perceptual-image","title":"High-Frequency aware Perceptual Image Enhancement","date":"2021-05-25","arxiv_id":"2105.11711","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-adaptive-representation-learning-for","title":"Content-adaptive Representation Learning for Fast Image Super-resolution","date":"2021-05-20","arxiv_id":"2105.09645","repositories_listed":0,"syntology":null},{"url":null,"slug":"xcycles-backprojection-acoustic-super","title":"XCycles Backprojection Acoustic Super-Resolution","date":"2021-05-19","arxiv_id":"2105.09128","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-frequency-domain-constraint-for-synthetic-x","title":"A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution","date":"2021-05-14","arxiv_id":"2105.06887","repositories_listed":0,"syntology":null},{"url":null,"slug":"twist-gan-towards-wavelet-transform-and","title":"TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution","date":"2021-04-20","arxiv_id":"2104.10268","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-agnostic-real-world-image-super","title":"Kernel Adversarial Learning for Real-world Image Super-resolution","date":"2021-04-19","arxiv_id":"2104.09008","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-architecture-search-for-image-super","title":"Neural Architecture Search for Image Super-Resolution Using Densely Constructed Search Space: DeCoNAS","date":"2021-04-19","arxiv_id":"2104.09048","repositories_listed":0,"syntology":null},{"url":null,"slug":"srr-net-a-super-resolution-involved","title":"SRR-Net: A Super-Resolution-Involved Reconstruction Method for High Resolution MR Imaging","date":"2021-04-13","arxiv_id":"2104.05901","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-a-better-loss-function-for-image","title":"Training a Task-Specific Image Reconstruction Loss","date":"2021-03-26","arxiv_id":"2103.14616","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-image-super-resolution-with-multi","title":"Lightweight Image Super-Resolution with Multi-scale Feature Interaction Network","date":"2021-03-24","arxiv_id":"2103.13028","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-super-resolution-in","title":"Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band Settings","date":"2021-03-19","arxiv_id":"2103.10614","repositories_listed":0,"syntology":null},{"url":null,"slug":"generic-perceptual-loss-for-modeling","title":"Generic Perceptual Loss for Modeling Structured Output Dependencies","date":"2021-03-18","arxiv_id":"2103.10571","repositories_listed":0,"syntology":null},{"url":null,"slug":"shipsrdet-an-end-to-end-remote-sensing-ship","title":"ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation","date":"2021-03-17","arxiv_id":"2103.09699","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-frequency-aware-dynamic-network-for","title":"Learning Frequency-aware Dynamic Network for Efficient Super-Resolution","date":"2021-03-15","arxiv_id":"2103.08357","repositories_listed":0,"syntology":null},{"url":null,"slug":"d2c-sr-a-divergence-to-convergence-approach-1","title":"D2C-SR: A Divergence to Convergence Approach for Image Super-Resolution","date":"2021-03-12","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-spectral-feedback-network-for-super","title":"Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery","date":"2021-03-07","arxiv_id":"2103.04354","repositories_listed":0,"syntology":null},{"url":null,"slug":"shuffleunet-super-resolution-of-diffusion","title":"ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning","date":"2021-02-25","arxiv_id":"2102.12898","repositories_listed":0,"syntology":null},{"url":null,"slug":"tchebichef-transform-domain-based-deep","title":"Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution","date":"2021-02-21","arxiv_id":"2102.10640","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-review-of-deep-learning-based","title":"A Comprehensive Review of Deep Learning-based Single Image Super-resolution","date":"2021-02-18","arxiv_id":"2102.09351","repositories_listed":0,"syntology":null},{"url":null,"slug":"selfie-periocular-verification-using-an","title":"Selfie Periocular Verification using an Efficient Super-Resolution Approach","date":"2021-02-16","arxiv_id":"2102.08449","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-model-for-hallucinating-diverse","title":"A Generative Model for Hallucinating Diverse Versions of Super Resolution Images","date":"2021-02-12","arxiv_id":"2102.06624","repositories_listed":0,"syntology":null},{"url":"/paper/sr-affine-high-quality-3d-hand-model","slug":"sr-affine-high-quality-3d-hand-model","title":"I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling","date":"2021-02-07","arxiv_id":"2102.03725","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-world-super-resolution-of-face-images","title":"Real-World Super-Resolution of Face-Images from Surveillance Cameras","date":"2021-02-05","arxiv_id":"2102.03113","repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-assessment-of-super-resolved","title":"Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views","date":"2021-01-25","arxiv_id":"2101.10396","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-restoration-by-solving-ivp","title":"Progressive Image Super-Resolution via Neural Differential Equation","date":"2021-01-22","arxiv_id":"2101.08987","repositories_listed":0,"syntology":null},{"url":null,"slug":"trilevel-neural-architecture-search-for","title":"Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution","date":"2021-01-17","arxiv_id":"2101.06658","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-super-resolution","title":"Single Image Super-Resolution","date":"2021-01-08","arxiv_id":"2101.02802","repositories_listed":0,"syntology":null},{"url":null,"slug":"more-reliable-ai-solution-breast-ultrasound","title":"More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination","date":"2021-01-07","arxiv_id":"2101.02639","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-in-vision-a-survey","title":"Transformers in Vision: A Survey","date":"2021-01-04","arxiv_id":"2101.01169","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-ultra-high-definition-image","title":"Benchmarking Ultra-High-Definition Image Super-Resolution","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"context-reasoning-attention-network-for-image","title":"Context Reasoning Attention Network for Image Super-Resolution","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-high-pass-filtering-and-multi","title":"Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"not-so-big-gan-generating-high-fidelity","title":"not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-image-upsampling","title":"Attention-based Image Upsampling","date":"2020-12-17","arxiv_id":"2012.09904","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-quality-assessment-for-image","title":"Learning-Based Quality Assessment for Image Super-Resolution","date":"2020-12-16","arxiv_id":"2012.08732","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-omni-frequency-region-adaptive","title":"Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution","date":"2020-12-11","arxiv_id":"2012.06131","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-guided-pore-detection-for","title":"Super-resolution Guided Pore Detection for Fingerprint Recognition","date":"2020-12-10","arxiv_id":"2012.05959","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-image-super-resolution-via-fusion-of","title":"Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors","date":"2020-12-07","arxiv_id":"2012.03417","repositories_listed":0,"syntology":null},{"url":null,"slug":"glean-generative-latent-bank-for-large-factor","title":"GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution","date":"2020-12-01","arxiv_id":"2012.00739","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-mpi-cross-scale-stereo-for-image-super","title":"Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images","date":"2020-11-30","arxiv_id":"2011.14631","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-super-resolution-with-a-switch","title":"Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images","date":"2020-11-29","arxiv_id":"2011.14380","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-progressive-fusion-learning-for","title":"Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution","date":"2020-11-24","arxiv_id":"2011.11865","repositories_listed":0,"syntology":null},{"url":null,"slug":"cryo-zssr-multiple-image-super-resolution","title":"Cryo-ZSSR: multiple-image super-resolution based on deep internal learning","date":"2020-11-22","arxiv_id":"2011.11020","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-super-resolution-networks-with","title":"Interpreting Super-Resolution Networks with Local Attribution Maps","date":"2020-11-22","arxiv_id":"2011.11036","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-device-text-image-super-resolution","title":"On-Device Text Image Super Resolution","date":"2020-11-20","arxiv_id":"2011.10251","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-u-net-for-super-resolution-with-shuffle","title":"Dense U-net for super-resolution with shuffle pooling layer","date":"2020-11-11","arxiv_id":"2011.05490","repositories_listed":0,"syntology":null},{"url":null,"slug":"invertible-cnn-based-super-resolution-with","title":"Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets","date":"2020-11-11","arxiv_id":"2011.05586","repositories_listed":0,"syntology":null},{"url":null,"slug":"epsr-edge-profile-super-resolution","title":"EPSR: Edge Profile Super resolution","date":"2020-11-09","arxiv_id":"2011.05308","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-perceptive-gans-for-brain-mr-image-super","title":"Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain","date":"2020-11-09","arxiv_id":"2011.04145","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmented-equivariant-attention-networks-for","title":"Augmented Equivariant Attention Networks for Microscopy Image Reconstruction","date":"2020-11-06","arxiv_id":"2011.03633","repositories_listed":0,"syntology":null}],"record_sha256":"7bfe952acdf90fc579e63421020b6f036807a12829ddfeab8412e092a958da2b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}