{"total":354,"rows":[{"id":"2108.10257","title":"SwinIR: Image Restoration Using Swin Transformer","created":"2021-08-23","cited_by_count":1887,"area":null},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627,"area":null},{"id":"2102.02808","title":"Multi-Stage Progressive Image Restoration","created":"2021-02-04","cited_by_count":816,"area":null},{"id":"2103.14006","title":"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution","created":"2021-03-25","cited_by_count":529,"area":null},{"id":"2101.05278","title":"GAN Inversion: A Survey","created":"2021-01-14","cited_by_count":367,"area":null},{"id":"2401.13627","title":"Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild","created":"2024-01-24","cited_by_count":286,"area":null},{"id":"2103.12828","title":"Learning to Optimize: A Primer and A Benchmark","created":"2021-03-23","cited_by_count":208,"area":null},{"id":"2105.14951","title":"SNIPS: Solving Noisy Inverse Problems Stochastically","created":"2021-05-31","cited_by_count":191,"area":null},{"id":"2301.11699","title":"Image Restoration with Mean-Reverting Stochastic Differential Equations","created":"2023-01-27","cited_by_count":183,"area":null},{"id":"2401.16468","title":"InstructIR: High-Quality Image Restoration Following Human Instructions","created":"2024-01-29","cited_by_count":140,"area":null},{"id":"2102.07944","title":"Deep Equilibrium Architectures for Inverse Problems in Imaging","created":"2021-02-16","cited_by_count":127,"area":null},{"id":"2204.13348","title":"Deep Generalized Unfolding Networks for Image Restoration","created":"2022-04-28","cited_by_count":121,"area":null},{"id":"2203.01441","title":"3D Common Corruptions and Data Augmentation","created":"2022-03-02","cited_by_count":119,"area":null},{"id":"2306.13090","title":"PromptIR: Prompting for All-in-One Blind Image Restoration","created":"2023-06-22","cited_by_count":114,"area":null},{"id":"2403.14614","title":"AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation","created":"2024-03-21","cited_by_count":111,"area":null},{"id":"2110.03220","title":"Gradient Step Denoiser for convergent Plug-and-Play","created":"2021-10-07","cited_by_count":108,"area":null},{"id":"2103.04715","title":"Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie","created":"2021-03-08","cited_by_count":97,"area":null},{"id":"2201.10700","title":"Deep Image Deblurring: A Survey","created":"2022-01-26","cited_by_count":94,"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":"2210.01427","title":"Accurate Image Restoration with Attention Retractable Transformer","created":"2022-10-04","cited_by_count":85,"area":null},{"id":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"area":null},{"id":"2201.13256","title":"Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization","created":"2022-01-31","cited_by_count":80,"area":null},{"id":"2209.11345","title":"Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration","created":"2022-09-22","cited_by_count":73,"area":null},{"id":"2104.06977","title":"Discrete Cosine Transform Network for Guided Depth Map Super-Resolution","created":"2021-04-14","cited_by_count":71,"area":null},{"id":"2301.03362","title":"Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --","created":"2023-01-09","cited_by_count":71,"area":null},{"id":"2309.05239","title":"HAT: Hybrid Attention Transformer for Image Restoration","created":"2023-09-11","cited_by_count":66,"area":null},{"id":"2112.10175","title":"On Efficient Transformer-Based Image Pre-training for Low-Level Vision","created":"2021-12-19","cited_by_count":64,"area":null},{"id":"2304.10244","title":"Omni Aggregation Networks for Lightweight Image Super-Resolution","created":"2023-04-20","cited_by_count":62,"area":null},{"id":"2112.04491","title":"Improving Image Restoration by Revisiting Global Information Aggregation","created":"2021-12-08","cited_by_count":61,"area":null},{"id":"2410.15067","title":"A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends","created":"2024-10-19","cited_by_count":60,"area":null},{"id":"2103.12716","title":"UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution","created":"2021-03-23","cited_by_count":57,"area":null},{"id":"2310.11881","title":"A Comparative Study of Image Restoration Networks for General Backbone Network Design","created":"2023-10-18","cited_by_count":57,"area":null},{"id":"2203.06074","title":"TAPE: Task-Agnostic Prior Embedding for Image Restoration","created":"2022-03-11","cited_by_count":55,"area":null},{"id":"2109.06620","title":"Dynamic Attentive Graph Learning for Image Restoration","created":"2021-09-14","cited_by_count":48,"area":null},{"id":"2112.00002","title":"Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields","created":"2021-11-27","cited_by_count":48,"area":null},{"id":"2207.06680","title":"Equivariant Hypergraph Diffusion Neural Operators","created":"2022-07-14","cited_by_count":47,"area":null},{"id":"2310.12848","title":"Neural Degradation Representation Learning for All-In-One Image Restoration","created":"2023-10-19","cited_by_count":46,"area":null},{"id":"2205.15175","title":"ShuffleMixer: An Efficient ConvNet for Image Super-Resolution","created":"2022-05-30","cited_by_count":45,"area":null},{"id":"2103.04584","title":"Deep Gradient Projection Networks for Pan-sharpening","created":"2021-03-08","cited_by_count":44,"area":null},{"id":"2505.12630","title":"Degradation-Aware Feature Perturbation for All-in-One Image Restoration","created":"2025-05-19","cited_by_count":41,"area":null},{"id":"2103.05872","title":"Sampling methods for efficient training of graph convolutional networks: A survey","created":"2021-03-10","cited_by_count":40,"area":null},{"id":"2108.08286","title":"Deep Reparametrization of Multi-Frame Super-Resolution and Denoising","created":"2021-08-18","cited_by_count":39,"area":null},{"id":"2108.12841","title":"Rethinking Deep Image Prior for Denoising","created":"2021-08-29","cited_by_count":38,"area":null},{"id":"2110.09766","title":"Memory-Augmented Deep Unfolding Network for Compressive Sensing","created":"2021-10-19","cited_by_count":38,"area":null},{"id":"2303.06440","title":"Xformer: Hybrid X-Shaped Transformer for Image Denoising","created":"2023-03-11","cited_by_count":37,"area":null},{"id":"2103.09962","title":"DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring","created":"2021-03-18","cited_by_count":36,"area":null},{"id":"2312.17334","title":"Improving Image Restoration through Removing Degradations in Textual Representations","created":"2023-12-28","cited_by_count":36,"area":null},{"id":"2303.02881","title":"KBNet: Kernel Basis Network for Image Restoration","created":"2023-03-06","cited_by_count":33,"area":null},{"id":"2107.07225","title":"COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing","created":"2021-07-15","cited_by_count":32,"area":null},{"id":"2309.07835","title":"Learning to Warm-Start Fixed-Point Optimization Algorithms","created":"2023-09-14","cited_by_count":32,"area":null},{"id":"2204.02844","title":"Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training","created":"2022-04-06","cited_by_count":29,"area":null},{"id":"2304.11928","title":"Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving","created":"2023-04-24","cited_by_count":29,"area":null},{"id":"2112.06242","title":"Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow","created":"2021-12-12","cited_by_count":28,"area":null},{"id":"2211.12051","title":"Adaptive Dynamic Filtering Network for Image Denoising","created":"2022-11-22","cited_by_count":28,"area":null},{"id":"2312.06162","title":"Textual Prompt Guided Image Restoration","created":"2023-12-11","cited_by_count":27,"area":null},{"id":"2110.09807","title":"Learning to Learn Graph Topologies","created":"2021-10-19","cited_by_count":26,"area":null},{"id":"2105.12227","title":"Learning a Model-Driven Variational Network for Deformable Image Registration","created":"2021-05-25","cited_by_count":25,"area":null},{"id":"2310.14344","title":"What's in a Prior? Learned Proximal Networks for Inverse Problems","created":"2023-10-22","cited_by_count":25,"area":null},{"id":"2103.01648","title":"Solving Inverse Problems by Joint Posterior Maximization with Autoencoding Prior","created":"2021-03-02","cited_by_count":24,"area":null},{"id":"2102.09351","title":"A Comprehensive Review of Deep Learning-based Single Image Super-resolution","created":"2021-02-18","cited_by_count":23,"area":null},{"id":"2503.13165","title":"From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective","created":"2025-03-17","cited_by_count":23,"area":null},{"id":"2106.03668","title":"Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition","created":"2021-06-07","cited_by_count":22,"area":null},{"id":"2205.13051","title":"Online Deep Equilibrium Learning for Regularization by Denoising","created":"2022-05-25","cited_by_count":22,"area":null},{"id":"2209.08240","title":"Deep Plug-and-Play Prior for Hyperspectral Image Restoration","created":"2022-09-17","cited_by_count":22,"area":null},{"id":"2211.06891","title":"Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging","created":"2022-11-13","cited_by_count":22,"area":null},{"id":"2407.20928","title":"UniProcessor: A Text-induced Unified Low-level Image Processor","created":"2024-07-30","cited_by_count":22,"area":null},{"id":"2104.06191","title":"Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts","created":"2021-04-13","cited_by_count":21,"area":null},{"id":"2207.09313","title":"Content-aware Scalable Deep Compressed Sensing","created":"2022-07-19","cited_by_count":21,"area":null},{"id":"2308.03021","title":"All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation","created":"2023-08-06","cited_by_count":21,"area":null},{"id":"2204.08913","title":"Self-Calibrated Efficient Transformer for Lightweight Super-Resolution","created":"2022-04-19","cited_by_count":20,"area":null},{"id":"2104.06594","title":"Learning Regularization Parameters of Inverse Problems via Deep Neural Networks","created":"2021-04-14","cited_by_count":19,"area":null},{"id":"2104.10348","title":"Fixed-Point and Objective Convergence of Plug-and-Play Algorithms","created":"2021-04-21","cited_by_count":19,"area":null},{"id":"2102.13011","title":"Learning for Unconstrained Space-Time Video Super-Resolution","created":"2021-02-25","cited_by_count":18,"area":null},{"id":"2203.08921","title":"Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution","created":"2022-03-16","cited_by_count":18,"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":"2303.09735","title":"SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution","created":"2023-03-17","cited_by_count":18,"area":null},{"id":"2305.05867","title":"Optical Aberration Correction in Postprocessing using Imaging Simulation","created":"2023-05-10","cited_by_count":18,"area":null},{"id":"2404.01692","title":"Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss","created":"2024-04-02","cited_by_count":18,"area":null},{"id":"2407.19139","title":"Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration","created":"2024-07-27","cited_by_count":18,"area":null},{"id":"2104.14090","title":"Feasibility-based Fixed Point Networks","created":"2021-04-29","cited_by_count":17,"area":null},{"id":"2105.06878","title":"End-to-end Alternating Optimization for Blind Super Resolution","created":"2021-05-14","cited_by_count":17,"area":null},{"id":"2310.11838","title":"Equivariant Bootstrapping for Uncertainty Quantification in Imaging Inverse Problems","created":"2023-10-18","cited_by_count":17,"area":null},{"id":"2312.15701","title":"Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration","created":"2023-12-25","cited_by_count":17,"area":null},{"id":"2402.01779","title":"Plug-and-Play image restoration with Stochastic deNOising REgularization","created":"2024-02-01","cited_by_count":17,"area":null},{"id":"2102.06155","title":"Learning local regularization for variational image restoration","created":"2021-02-11","cited_by_count":16,"area":null},{"id":"2103.05961","title":"COLA-Net: Collaborative Attention Network for Image Restoration","created":"2021-03-10","cited_by_count":16,"area":null},{"id":"2108.04165","title":"No-Reference Image Quality Assessment by Hallucinating Pristine Features","created":"2021-08-09","cited_by_count":16,"area":null},{"id":"2105.03085","title":"Toward Interactive Modulation for Photo-Realistic Image Restoration","created":"2021-05-07","cited_by_count":15,"area":null},{"id":"2303.07271","title":"Provably Convergent Plug-and-Play Quasi-Newton Methods","created":"2023-03-09","cited_by_count":15,"area":null},{"id":"2306.16060","title":"Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensing","created":"2023-06-28","cited_by_count":15,"area":null},{"id":"2404.04878","title":"CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data","created":"2024-04-07","cited_by_count":15,"area":null},{"id":"2410.05601","title":"ReFIR: Grounding Large Restoration Models with Retrieval Augmentation","created":"2024-10-08","cited_by_count":15,"area":null},{"id":"2103.13634","title":"Asymmetric CNN for image super-resolution","created":"2021-03-25","cited_by_count":14,"area":null},{"id":"2201.06133","title":"On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent","created":"2022-01-16","cited_by_count":14,"area":null},{"id":"2210.05361","title":"Uncertainty-Aware Unsupervised Image Deblurring with Deep Residual Prior","created":"2022-10-09","cited_by_count":14,"area":null},{"id":"2306.03466","title":"Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems","created":"2023-06-06","cited_by_count":14,"area":null},{"id":"2312.01677","title":"Multi-task Image Restoration Guided By Robust DINO Features","created":"2023-12-04","cited_by_count":14,"area":null},{"id":"2312.11232","title":"Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring","created":"2023-12-18","cited_by_count":14,"area":null},{"id":"2106.15910","title":"Graph Signal Restoration Using Nested Deep Algorithm Unrolling","created":"2021-06-30","cited_by_count":13,"area":null},{"id":"2112.05756","title":"Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding","created":"2021-12-10","cited_by_count":13,"area":null}],"next_cursor":"100"}