{"total":61,"rows":[{"id":"2104.00416","title":"Unsupervised Degradation Representation Learning for Blind Super-Resolution","created":"2021-04-01","cited_by_count":177,"area":null},{"id":"2102.07074","title":"TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up","created":"2021-02-14","cited_by_count":162,"area":null},{"id":"2203.14216","title":"Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution","created":"2022-03-27","cited_by_count":96,"area":null},{"id":"2107.03055","title":"Blind Image Super-Resolution: A Survey and Beyond","created":"2021-07-07","cited_by_count":93,"area":null},{"id":"2103.15977","title":"Flow-based Kernel Prior with Application to Blind Super-Resolution","created":"2021-03-29","cited_by_count":74,"area":null},{"id":"2108.05302","title":"Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution","created":"2021-08-11","cited_by_count":52,"area":null},{"id":"2202.07508","title":"Deep Constrained Least Squares for Blind Image Super-Resolution","created":"2022-02-15","cited_by_count":47,"area":null},{"id":"2204.00367","title":"Learning to Deblur using Light Field Generated and Real Defocus Images","created":"2022-04-01","cited_by_count":46,"area":null},{"id":"2107.00708","title":"Blind Image Super-Resolution via Contrastive Representation Learning","created":"2021-07-01","cited_by_count":32,"area":null},{"id":"2107.10433","title":"MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking","created":"2021-07-22","cited_by_count":32,"area":null},{"id":"2104.09556","title":"Removing Diffraction Image Artifacts in Under-Display Camera via Dynamic Skip Connection Network","created":"2021-04-19","cited_by_count":31,"area":null},{"id":"2211.16928","title":"Knowledge Distillation based Degradation Estimation for Blind Super-Resolution","created":"2022-11-30","cited_by_count":28,"area":"distill"},{"id":"2105.01237","title":"COMISR: Compression-Informed Video Super-Resolution","created":"2021-05-04","cited_by_count":26,"area":null},{"id":"2207.01074","title":"Variational Deep Image Restoration","created":"2022-07-03","cited_by_count":24,"area":null},{"id":"2109.14335","title":"A Systematic Survey of Deep Learning-based Single-Image Super-Resolution","created":"2021-09-29","cited_by_count":19,"area":null},{"id":"2209.10305","title":"KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution","created":"2022-09-21","cited_by_count":18,"area":null},{"id":"2404.15620","title":"A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution","created":"2024-04-24","cited_by_count":15,"area":"diffusion"},{"id":"2109.03444","title":"Toward Real-World Super-Resolution via Adaptive Downsampling Models","created":"2021-09-08","cited_by_count":14,"area":null},{"id":"2306.09008","title":"Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal","created":"2023-06-15","cited_by_count":12,"area":null},{"id":"2108.07895","title":"Adaptive Convolutions with Per-pixel Dynamic Filter Atom","created":"2021-08-17","cited_by_count":11,"area":null},{"id":"2207.13963","title":"Meta-Learning based Degradation Representation for Blind Super-Resolution","created":"2022-07-28","cited_by_count":11,"area":null},{"id":"2206.06214","title":"Real-World Light Field Image Super-Resolution via Degradation Modulation","created":"2022-06-13","cited_by_count":8,"area":null},{"id":"2208.13436","title":"Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution","created":"2022-08-29","cited_by_count":8,"area":null},{"id":"2305.03387","title":"AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions","created":"2023-05-05","cited_by_count":8,"area":null},{"id":"2403.00628","title":"Region-Adaptive Transform with Segmentation Prior for Image Compression","created":"2024-03-01","cited_by_count":8,"area":null},{"id":"2405.07648","title":"CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution","created":"2024-05-13","cited_by_count":8,"area":"diffusion"},{"id":"2104.03926","title":"Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations","created":"2021-04-08","cited_by_count":7,"area":null},{"id":"2110.12151","title":"Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution","created":"2021-10-23","cited_by_count":7,"area":null},{"id":"2203.10489","title":"TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing","created":"2022-03-20","cited_by_count":7,"area":null},{"id":"2207.00943","title":"Degradation-Guided Meta-Restoration Network for Blind Super-Resolution","created":"2022-07-03","cited_by_count":7,"area":null},{"id":"2304.03542","title":"Better \"CMOS\" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution","created":"2023-04-07","cited_by_count":7,"area":null},{"id":"2308.05787","title":"Temporally-Adaptive Models for Efficient Video Understanding","created":"2023-08-10","cited_by_count":7,"area":null},{"id":"2406.08896","title":"Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation","created":"2024-06-13","cited_by_count":7,"area":null},{"id":"2201.00392","title":"Fast and High-Quality Image Denoising via Malleable Convolutions","created":"2022-01-02","cited_by_count":6,"area":null},{"id":"2212.01624","title":"Learning Detail-Structure Alternative Optimization for Blind Super-Resolution","created":"2022-12-03","cited_by_count":6,"area":null},{"id":"2112.07315","title":"Kernel-aware Burst Blind Super-Resolution","created":"2021-12-14","cited_by_count":5,"area":null},{"id":"2207.09228","title":"Image Super-Resolution with Deep Dictionary","created":"2022-07-19","cited_by_count":5,"area":null},{"id":"2103.15290","title":"Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution","created":"2021-03-29","cited_by_count":4,"area":null},{"id":"2210.07751","title":"Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows","created":"2022-10-14","cited_by_count":4,"area":null},{"id":"2411.05885","title":"Alternative Learning Paradigms for Image Quality Transfer","created":"2024-11-08","cited_by_count":4,"area":null},{"id":"2110.14638","title":"Improving Super-Resolution Performance using Meta-Attention Layers","created":"2021-10-27","cited_by_count":2,"area":null},{"id":"2310.10413","title":"Image super-resolution via dynamic network","created":"2023-10-16","cited_by_count":2,"area":null},{"id":"2406.16459","title":"Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution","created":"2024-06-24","cited_by_count":2,"area":null},{"id":"2603.04745","title":"Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset","created":"2026-03-05","cited_by_count":2,"area":null},{"id":"2212.01628","title":"Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution","created":"2022-12-03","cited_by_count":1,"area":null},{"id":"2302.08478","title":"Kernelized Back-Projection Networks for Blind Super Resolution","created":"2023-02-16","cited_by_count":1,"area":null},{"id":"2307.12014","title":"NLCUnet: Single-Image Super-Resolution Network with Hairline Details","created":"2023-07-22","cited_by_count":1,"area":null},{"id":"2402.15704","title":"Adaptive Convolutional Neural Network for Image Super-resolution","created":"2024-02-24","cited_by_count":1,"area":null},{"id":"2407.01299","title":"Preserving Full Degradation Details for Blind Image Super-Resolution","created":"2024-07-01","cited_by_count":1,"area":null},{"id":"2506.07705","title":"Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations","created":"2025-06-09","cited_by_count":1,"area":null},{"id":"2509.22692","title":"Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects","created":"2025-09-19","cited_by_count":1,"area":null},{"id":"2201.02314","title":"RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images","created":"2022-01-07","cited_by_count":0,"area":null},{"id":"2211.05018","title":"The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks","created":"2022-11-09","cited_by_count":0,"area":null},{"id":"2304.00330","title":"Multi-view reconstruction of bullet time effect based on improved NSFF model","created":"2023-04-01","cited_by_count":0,"area":null},{"id":"2402.06117","title":"Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring","created":"2024-02-09","cited_by_count":0,"area":null},{"id":"2408.05440","title":"Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution","created":"2024-08-10","cited_by_count":0,"area":null},{"id":"2409.12165","title":"NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning","created":"2024-09-17","cited_by_count":0,"area":null},{"id":"2412.16459","title":"Rethinking Model Redundancy for Low-light Image Enhancement","created":"2024-12-21","cited_by_count":0,"area":null},{"id":"2505.09196","title":"PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2506.17027","title":"Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns","created":"2025-06-20","cited_by_count":0,"area":null},{"id":"2506.22710","title":"LightBSR: Towards Lightweight Blind Super-Resolution via Discriminative Implicit Degradation Representation Learning","created":"2025-06-28","cited_by_count":0,"area":null}],"next_cursor":null}