{"list":"other","total":51,"rows":[{"id":"2112.10752","title":"High-Resolution Image Synthesis with Latent Diffusion Models","created":"2021-12-20","cited_by_count":18863},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"1711.07971","title":"Non-local Neural Networks","created":"2017-11-21","cited_by_count":2293},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887},{"id":"1707.02921","title":"Enhanced Deep Residual Networks for Single Image Super-Resolution","created":"2017-07-10","cited_by_count":1552},{"id":"2104.07636","title":"Image Super-Resolution via Iterative Refinement","created":"2021-04-15","cited_by_count":1511},{"id":"1807.02758","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","created":"2018-07-08","cited_by_count":1264},{"id":"1809.00219","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks","created":"2018-09-01","cited_by_count":1254},{"id":"1501.00092","title":"Image Super-Resolution Using Deep Convolutional Networks","created":"2014-12-31","cited_by_count":1224},{"id":"2101.01169","title":"Transformers in Vision: A Survey","created":"2021-01-04","cited_by_count":1013},{"id":"2201.11793","title":"Denoising Diffusion Restoration Models","created":"2022-01-27","cited_by_count":963},{"id":"1511.04587","title":"Accurate Image Super-Resolution Using Very Deep Convolutional Networks","created":"2015-11-14","cited_by_count":903},{"id":"1606.05328","title":"Conditional Image Generation with PixelCNN Decoders","created":"2016-06-16","cited_by_count":862},{"id":"1802.08797","title":"Residual Dense Network for Image Super-Resolution","created":"2018-02-24","cited_by_count":799},{"id":"2110.04627","title":"Vector-quantized Image Modeling with Improved VQGAN","created":"2021-10-09","cited_by_count":658},{"id":"2108.02938","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","created":"2021-08-06","cited_by_count":635},{"id":"2205.04437","title":"Activating More Pixels in Image Super-Resolution Transformer","created":"2022-05-09","cited_by_count":438},{"id":"1904.00523","title":"Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model","created":"2019-04-01","cited_by_count":403},{"id":"1804.02815","title":"Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform","created":"2018-04-09","cited_by_count":371},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"2003.03808","title":"PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models","created":"2020-03-08","cited_by_count":358},{"id":"1910.06180","title":"KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment","created":"2019-10-14","cited_by_count":355},{"id":"1510.04389","title":"Sketch-based Manga Retrieval using Manga109 Dataset","created":"2015-10-15","cited_by_count":329},{"id":"2112.05146","title":"Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction","created":"2021-12-09","cited_by_count":307},{"id":"1712.06087","title":"\"Zero-Shot\" Super-Resolution using Deep Internal Learning","created":"2017-12-17","cited_by_count":286},{"id":"2008.08767","title":"Single Image Super-Resolution via a Holistic Attention Network","created":"2020-08-20","cited_by_count":265},{"id":"2008.01928","title":"Component Divide-and-Conquer for Real-World Image Super-Resolution","created":"2020-08-05","cited_by_count":246},{"id":"1903.10082","title":"Residual Non-local Attention Networks for Image Restoration","created":"2019-03-24","cited_by_count":236},{"id":"1909.11856","title":"Lightweight Image Super-Resolution with Information Multi-distillation Network","created":"2019-09-26","cited_by_count":227},{"id":"1612.07919","title":"EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis","created":"2016-12-23","cited_by_count":178},{"id":"1806.02919","title":"Non-Local Recurrent Network for Image Restoration","created":"2018-06-07","cited_by_count":160},{"id":"2203.06697","title":"Efficient Long-Range Attention Network for Image Super-resolution","created":"2022-03-13","cited_by_count":151},{"id":"1809.00437","title":"Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks","created":"2018-09-03","cited_by_count":133},{"id":"2009.11551","title":"Residual Feature Distillation Network for Lightweight Image Super-Resolution","created":"2020-09-24","cited_by_count":122},{"id":"2003.13081","title":"Structure-Preserving Super Resolution with Gradient Guidance","created":"2020-03-29","cited_by_count":116},{"id":"2202.13142","title":"Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors","created":"2022-02-26","cited_by_count":100},{"id":"1709.02576","title":"Deep learning for undersampled MRI reconstruction","created":"2017-09-08","cited_by_count":91},{"id":"1812.01608","title":"Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling","created":"2018-12-04","cited_by_count":74},{"id":"2106.00783","title":"Fourier Space Losses for Efficient Perceptual Image Super-Resolution","created":"2021-06-01","cited_by_count":73},{"id":"1904.02816","title":"Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning","created":"2019-04-04","cited_by_count":60},{"id":"2402.10491","title":"Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation","created":"2024-02-16","cited_by_count":51},{"id":"1702.00783","title":"Pixel Recursive Super Resolution","created":"2017-02-02","cited_by_count":44},{"id":"2209.14145","title":"Multi-scale Attention Network for Single Image Super-Resolution","created":"2022-09-28","cited_by_count":29},{"id":"2201.03794","title":"Efficient Non-Local Contrastive Attention for Image Super-Resolution","created":"2022-01-11","cited_by_count":25},{"id":"1604.08671","title":"Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution","created":"2016-04-29","cited_by_count":22},{"id":"2109.14335","title":"A Systematic Survey of Deep Learning-based Single-Image Super-Resolution","created":"2021-09-29","cited_by_count":19},{"id":"1810.01831","title":"An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks","created":"2018-10-03","cited_by_count":3},{"id":"2302.03406","title":"High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets","created":"2023-02-07","cited_by_count":3}],"next_cursor":null}