{"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/15","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":15,"pages_in_order":16,"rows_per_page":100,"rows":[1401,1500],"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/14","next":"/task/image-super-resolution/papers/16","papers":[{"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"medical-image-super-resolution-method-based","title":"Medical image super-resolution method based on dense blended attention network","date":"2019-05-13","arxiv_id":"1905.05084","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-image-super-resolution-state-of-the","title":"Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution","date":"2019-05-07","arxiv_id":"1905.02462","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-opportunities-for-efficiency-in","title":"Understanding Opportunities for Efficiency in Single-image Super Resolution Networks","date":"2019-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-based-generative-adversarial","title":"Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators","date":"2019-04-24","arxiv_id":"1904.10654","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-transform-domain-image-super","title":"Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks","date":"2019-04-22","arxiv_id":"1904.10082","repositories_listed":0,"syntology":null},{"url":null,"slug":"process-of-image-super-resolution","title":"Process of image super-resolution","date":"2019-04-17","arxiv_id":"1904.08396","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-convolutional-neural-network","title":"Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images","date":"2019-04-16","arxiv_id":"1904.07470","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":"/paper/pirm2018-challenge-on-spectral-image-super","slug":"pirm2018-challenge-on-spectral-image-super","title":"PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study","date":"2019-04-01","arxiv_id":"1904.00540","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-real-world-single-image-super","title":"Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model","date":"2019-04-01","arxiv_id":"1904.00523","repositories_listed":0,"syntology":null},{"url":null,"slug":"proximal-splitting-networks-for-image","title":"Proximal Splitting Networks for Image Restoration","date":"2019-03-17","arxiv_id":"1903.07154","repositories_listed":0,"syntology":null},{"url":"/paper/flickr1024-a-dataset-for-stereo-image-super","slug":"flickr1024-a-dataset-for-stereo-image-super","title":"Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution","date":"2019-03-15","arxiv_id":"1903.06332","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-hypernetworks","title":"Hypernetwork functional image representation","date":"2019-02-27","arxiv_id":"1902.10404","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-of-brain-mri-images-using","title":"Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity","date":"2019-02-13","arxiv_id":"1902.04902","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-generative-adversarial-networks","title":"Progressive Generative Adversarial Networks for Medical Image Super resolution","date":"2019-02-06","arxiv_id":"1902.02144","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-inverse-problems-bounds-and","title":"Deep Learning for Inverse Problems: Bounds and Regularizers","date":"2019-01-31","arxiv_id":"1901.11352","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-generation-and-application-of-medical","title":"Medical Image Super-Resolution Using a Generative Adversarial Network","date":"2019-01-30","arxiv_id":"1902.00369","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-mr-image-super-resolution-via-channel","title":"Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network","date":"2019-01-19","arxiv_id":"1901.06484","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-deep-convolution-network-with","title":"Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution","date":"2019-01-18","arxiv_id":"1901.06405","repositories_listed":0,"syntology":null},{"url":"/paper/image-super-resolution-via-rl-csc-when","slug":"image-super-resolution-via-rl-csc-when","title":"Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding","date":"2018-12-31","arxiv_id":"1812.11950","repositories_listed":0,"syntology":null},{"url":null,"slug":"sredgenet-edge-enhanced-single-image-super","title":"SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network","date":"2018-12-18","arxiv_id":"1812.07174","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-super-resolution-using-binarized","title":"Efficient Super Resolution Using Binarized Neural Network","date":"2018-12-16","arxiv_id":"1812.06378","repositories_listed":0,"syntology":null},{"url":null,"slug":"wider-channel-attention-network-for-remote","title":"Wider Channel Attention Network for Remote Sensing Image Super-resolution","date":"2018-12-13","arxiv_id":"1812.05329","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-super-resolution-for-large-scale","title":"Efficient Super Resolution For Large-Scale Images Using Attentional GAN","date":"2018-12-12","arxiv_id":"1812.04821","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-degradation-learning-for-single","title":"Unsupervised Degradation Learning for Single Image Super-Resolution","date":"2018-12-11","arxiv_id":"1812.04240","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-deep-kriging-for-single-image","title":"Supervised Deep Kriging for Single-Image Super-Resolution","date":"2018-12-10","arxiv_id":"1812.04042","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":"bi-gans-st-for-perceptual-image-super","title":"Bi-GANs-ST for Perceptual Image Super-resolution","date":"2018-11-01","arxiv_id":"1811.00367","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-attention-and-multi-level-features","title":"Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution","date":"2018-10-16","arxiv_id":"1810.06935","repositories_listed":0,"syntology":null},{"url":"/paper/channel-splitting-network-for-single-mr-image","slug":"channel-splitting-network-for-single-mr-image","title":"Channel Splitting Network for Single MR Image Super-Resolution","date":"2018-10-15","arxiv_id":"1810.06453","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-super-resolution-using-vdsr-resnext-and","title":"Image Super-Resolution Using VDSR-ResNeXt and SRCGAN","date":"2018-10-10","arxiv_id":"1810.05731","repositories_listed":0,"syntology":null},{"url":null,"slug":"triple-attention-mixed-link-network-for","title":"Triple Attention Mixed Link Network for Single Image Super Resolution","date":"2018-10-08","arxiv_id":"1810.03254","repositories_listed":0,"syntology":null},{"url":null,"slug":"pirm-challenge-on-perceptual-image","title":"PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report","date":"2018-10-03","arxiv_id":"1810.01641","repositories_listed":0,"syntology":null},{"url":"/paper/superdepth-self-supervised-super-resolved","slug":"superdepth-self-supervised-super-resolved","title":"SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation","date":"2018-10-03","arxiv_id":"1810.01849","repositories_listed":0,"syntology":null},{"url":null,"slug":"theory-of-generative-deep-learning-probe","title":"Theory of Generative Deep Learning : Probe Landscape of Empirical Error via Norm Based Capacity Control","date":"2018-10-03","arxiv_id":"1810.01622","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-warship-combining-components-of-brain","title":"Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution","date":"2018-10-03","arxiv_id":"1810.01620","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-via-conditional-implicit","title":"Super-Resolution via Conditional Implicit Maximum Likelihood Estimation","date":"2018-10-02","arxiv_id":"1810.01406","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":"a-simple-framework-to-leverage-state-of-the","title":"A Simple Framework to Leverage State-Of-The-Art Single-Image Super-Resolution Methods to Restore Light Fields","date":"2018-09-27","arxiv_id":"1809.10449","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-based-low-rank-sparse-model-for-single","title":"Kernel based low-rank sparse model for single image super-resolution","date":"2018-09-27","arxiv_id":"1809.10582","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-denoising-and-super-resolution-using","title":"Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network","date":"2018-09-21","arxiv_id":"1809.08229","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-reconstruction-nets-for-image-super","title":"Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss","date":"2018-09-19","arxiv_id":"1809.07099","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-sub-bands-learning-with-clique","title":"Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution","date":"2018-09-12","arxiv_id":"1809.04508","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-mr-image-super-resolution-using","title":"Deep MR Image Super-Resolution Using Structural Priors","date":"2018-09-10","arxiv_id":"1809.03140","repositories_listed":0,"syntology":null},{"url":null,"slug":"optical-flow-super-resolution-based-on-image","title":"Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network","date":"2018-09-03","arxiv_id":"1809.00588","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-low-precision-deep-neural-networks","title":"Learning Sparse Low-Precision Neural Networks With Learnable Regularization","date":"2018-09-01","arxiv_id":"1809.00095","repositories_listed":0,"syntology":null},{"url":null,"slug":"srfeat-single-image-super-resolution-with","title":"SRFeat: Single Image Super-Resolution with Feature Discrimination","date":"2018-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"photorealistic-video-super-resolution","title":"Perceptual Video Super Resolution with Enhanced Temporal Consistency","date":"2018-07-20","arxiv_id":"1807.07930","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attention-based-approach-for-single-image","title":"An Attention-Based Approach for Single Image Super Resolution","date":"2018-07-18","arxiv_id":"1807.06779","repositories_listed":0,"syntology":null},{"url":null,"slug":"synnet-structure-preserving-fully","title":"SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis","date":"2018-06-29","arxiv_id":"1806.11475","repositories_listed":0,"syntology":null},{"url":null,"slug":"ct-image-super-resolution-using-3d","title":"CT-image Super Resolution Using 3D Convolutional Neural Network","date":"2018-06-24","arxiv_id":"1806.09074","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-importance-learning-for-improving","title":"Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network","date":"2018-06-05","arxiv_id":"1806.01576","repositories_listed":0,"syntology":null},{"url":"/paper/enhancing-the-spatial-resolution-of-stereo","slug":"enhancing-the-spatial-resolution-of-stereo","title":"Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-super-resolution-make-machine-see","title":"Feature Super-Resolution: Make Machine See More Clearly","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-residual-networks-with-a-fully-connected","title":"Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution","date":"2018-05-24","arxiv_id":"1805.10143","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-approach-of-interpolations-and-cnn","title":"A hybrid approach of interpolations and CNN to obtain super-resolution","date":"2018-05-23","arxiv_id":"1805.09400","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-receptive-field-networks-for-high-scale","title":"Large Receptive Field Networks for High-Scale Image Super-Resolution","date":"2018-04-22","arxiv_id":"1804.08181","repositories_listed":0,"syntology":null},{"url":null,"slug":"densely-connected-high-order-residual-network","title":"Densely Connected High Order Residual Network for Single Frame Image Super Resolution","date":"2018-04-16","arxiv_id":"1804.05902","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-sparse-dirichlet-net-for","title":"Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution","date":"2018-04-13","arxiv_id":"1804.05042","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-two-stage-3d-unet-framework-for-multi-class","title":"A two-stage 3D Unet framework for multi-class segmentation on full resolution image","date":"2018-04-12","arxiv_id":"1804.04341","repositories_listed":0,"syntology":null},{"url":null,"slug":"reference-conditioned-super-resolution-by","title":"Reference-Conditioned Super-Resolution by Neural Texture Transfer","date":"2018-04-10","arxiv_id":"1804.03360","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-driven-super-resolution-object-detection","title":"Task-Driven Super Resolution: Object Detection in Low-resolution Images","date":"2018-03-30","arxiv_id":"1803.11316","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-deep-learning-training-for-single","title":"Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction","date":"2018-03-23","arxiv_id":"1803.08840","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":"/paper/image-transformer","slug":"image-transformer","title":"Image Transformer","date":"2018-02-15","arxiv_id":"1802.05751","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-image-super-resolution-via-natural-image","title":"Deep Image Super Resolution via Natural Image Priors","date":"2018-02-08","arxiv_id":"1802.02721","repositories_listed":0,"syntology":null},{"url":null,"slug":"orthogonally-regularized-deep-networks-for","title":"Orthogonally Regularized Deep Networks For Image Super-resolution","date":"2018-02-06","arxiv_id":"1802.02018","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-mri-super-resolution-using-3d-deep","title":"Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks","date":"2018-01-08","arxiv_id":"1801.02728","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-with-deep-adaptive-image","title":"Super-Resolution with Deep Adaptive Image Resampling","date":"2017-12-18","arxiv_id":"1712.06463","repositories_listed":0,"syntology":null},{"url":null,"slug":"srpgan-perceptual-generative-adversarial","title":"SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution","date":"2017-12-16","arxiv_id":"1712.05927","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-frequency-domain-neural-network-for-fast","title":"A Frequency Domain Neural Network for Fast Image Super-resolution","date":"2017-12-08","arxiv_id":"1712.03037","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-inpainting-for-high-resolution-textures","title":"Image Inpainting for High-Resolution Textures using CNN Texture Synthesis","date":"2017-12-08","arxiv_id":"1712.03111","repositories_listed":0,"syntology":null},{"url":"/paper/super-fan-integrated-facial-landmark","slug":"super-fan-integrated-facial-landmark","title":"Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs","date":"2017-12-07","arxiv_id":"1712.02765","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sampling-networks","title":"Deep Sampling Networks","date":"2017-12-04","arxiv_id":"1712.00926","repositories_listed":0,"syntology":null},{"url":null,"slug":"blade-filter-learning-for-general-purpose","title":"BLADE: Filter Learning for General Purpose Computational Photography","date":"2017-11-29","arxiv_id":"1711.10700","repositories_listed":0,"syntology":null},{"url":null,"slug":"super-resolution-for-overhead-imagery-using","title":"Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning","date":"2017-11-28","arxiv_id":"1711.10312","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-inception-residual-laplacian-pyramid","title":"Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution","date":"2017-11-15","arxiv_id":"1711.05431","repositories_listed":0,"syntology":null}],"record_sha256":"36b944e856baa116a5507d351685fbc4e8fe69062fd4936b21bbb6300050b955","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}