{"total":235,"rows":[{"id":"2301.05187","title":"WIRE: Wavelet Implicit Neural Representations","created":"2023-01-05","cited_by_count":220,"area":null},{"id":"2105.14951","title":"SNIPS: Solving Noisy Inverse Problems Stochastically","created":"2021-05-31","cited_by_count":191,"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":"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":"2103.14756","title":"Equivariant Imaging: Learning Beyond the Range Space","created":"2021-03-26","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":"2306.01129","title":"White-Box Transformers via Sparse Rate Reduction","created":"2023-06-01","cited_by_count":80,"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":"2310.10835","title":"Provable Probabilistic Imaging using Score-Based Generative Priors","created":"2023-10-16","cited_by_count":51,"area":null},{"id":"2101.09552","title":"Stochastic Image Denoising by Sampling from the Posterior Distribution","created":"2021-01-23","cited_by_count":45,"area":null},{"id":"2211.12461","title":"A Neural-Network-Based Convex Regularizer for Inverse Problems","created":"2022-11-22","cited_by_count":41,"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":"2108.12654","title":"Self-supervised Neural Networks for Spectral Snapshot Compressive Imaging","created":"2021-08-28","cited_by_count":35,"area":null},{"id":"2409.06219","title":"Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning","created":"2024-09-10","cited_by_count":35,"area":null},{"id":"2106.03538","title":"End-to-end reconstruction meets data-driven regularization for inverse problems","created":"2021-06-07","cited_by_count":32,"area":null},{"id":"2104.01374","title":"Interpretable Unsupervised Diversity Denoising and Artefact Removal","created":"2021-04-03","cited_by_count":26,"area":null},{"id":"2312.01831","title":"Equivariant plug-and-play image reconstruction","created":"2023-12-04","cited_by_count":26,"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":"2103.10182","title":"Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms","created":"2021-03-18","cited_by_count":24,"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":"2107.11191","title":"Regularising Inverse Problems with Generative Machine Learning Models","created":"2021-07-22","cited_by_count":22,"area":null},{"id":"2205.12021","title":"PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization","created":"2022-05-24","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":"2206.14373","title":"Theoretical Perspectives on Deep Learning Methods in Inverse Problems","created":"2022-06-29","cited_by_count":22,"area":null},{"id":"2308.00942","title":"On the use of deep learning for phase recovery","created":"2023-08-02","cited_by_count":20,"area":null},{"id":"2104.03078","title":"Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution","created":"2021-04-07","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":"2109.08879","title":"FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal","created":"2021-09-18","cited_by_count":19,"area":null},{"id":"2101.09379","title":"SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees","created":"2021-01-22","cited_by_count":18,"area":null},{"id":"2506.05310","title":"Learning normalized image densities via dual score matching","created":"2025-06-05","cited_by_count":18,"area":null},{"id":"2105.08040","title":"Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement","created":"2021-05-17","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":"2510.01755","title":"Learning Regularization Functionals for Inverse Problems: A Comparative Study","created":"2025-10-02","cited_by_count":15,"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":"2203.15725","title":"Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey","created":"2022-03-29","cited_by_count":14,"area":null},{"id":"2302.03003","title":"OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing","created":"2023-02-06","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":"2402.01052","title":"Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation","created":"2024-02-01","cited_by_count":14,"area":null},{"id":"2607.00714","title":"Self-conditioned Flow Map Language Models via Fixed-point Flows","created":"2026-07-01","cited_by_count":14,"area":null},{"id":"2103.07609","title":"Untrained networks for compressive lensless photography","created":"2021-03-13","cited_by_count":13,"area":null},{"id":"2307.09441","title":"Convergent regularization in inverse problems and linear plug-and-play denoisers","created":"2023-07-18","cited_by_count":13,"area":null},{"id":"2308.02797","title":"Thin On-Sensor Nanophotonic Array Cameras","created":"2023-08-05","cited_by_count":13,"area":null},{"id":"2301.13731","title":"A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser","created":"2023-01-31","cited_by_count":12,"area":null},{"id":"2310.00133","title":"Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis","created":"2023-09-29","cited_by_count":11,"area":null},{"id":"2102.01026","title":"Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition","created":"2021-02-01","cited_by_count":10,"area":null},{"id":"2201.08157","title":"WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution","created":"2022-01-20","cited_by_count":10,"area":null},{"id":"2304.10536","title":"Learning Sparse and Low-Rank Priors for Image Recovery via Iterative Reweighted Least Squares Minimization","created":"2023-04-20","cited_by_count":10,"area":null},{"id":"2107.05533","title":"Deformation-Compensated Learning for Image Reconstruction without Ground Truth","created":"2021-07-12","cited_by_count":9,"area":null},{"id":"2112.03712","title":"Image Compressed Sensing Using Non-local Neural Network","created":"2021-12-07","cited_by_count":9,"area":null},{"id":"2203.10078","title":"Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors","created":"2022-03-18","cited_by_count":9,"area":null},{"id":"2310.07504","title":"PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction","created":"2023-10-11","cited_by_count":9,"area":null},{"id":"2312.07425","title":"Deep Internal Learning: Deep Learning from a Single Input","created":"2023-12-12","cited_by_count":9,"area":null},{"id":"2402.12292","title":"Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling","created":"2024-02-19","cited_by_count":9,"area":null},{"id":"2102.02485","title":"Image Restoration by Deep Projected GSURE","created":"2021-02-04","cited_by_count":8,"area":null},{"id":"2205.03519","title":"Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising","created":"2022-05-07","cited_by_count":8,"area":null},{"id":"2207.13200","title":"Deep Model-Based Architectures for Inverse Problems under Mismatched Priors","created":"2022-07-26","cited_by_count":8,"area":null},{"id":"2304.10250","title":"Revisiting Implicit Neural Representations in Low-Level Vision","created":"2023-04-20","cited_by_count":8,"area":null},{"id":"2305.12672","title":"Block Coordinate Plug-and-Play Methods for Blind Inverse Problems","created":"2023-05-22","cited_by_count":8,"area":null},{"id":"2308.13777","title":"Self-Supervised Scalable Deep Compressed Sensing","created":"2023-08-26","cited_by_count":8,"area":null},{"id":"2310.03546","title":"Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models","created":"2023-10-05","cited_by_count":8,"area":null},{"id":"2505.23180","title":"Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging","created":"2025-05-29","cited_by_count":8,"area":null},{"id":"2204.13940","title":"PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent","created":"2022-04-29","cited_by_count":7,"area":null},{"id":"2206.04797","title":"Memory-efficient model-based deep learning with convergence and robustness guarantees","created":"2022-06-06","cited_by_count":7,"area":null},{"id":"2304.08990","title":"A Comparison of Image Denoising Methods","created":"2023-04-18","cited_by_count":7,"area":null},{"id":"2507.11461","title":"Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descent","created":"2025-07-15","cited_by_count":7,"area":null},{"id":"2511.16854","title":"MRI Super-Resolution with Deep Learning: A Comprehensive Survey","created":"2025-11-20","cited_by_count":7,"area":null},{"id":"2106.10195","title":"Non-Iterative Phase Retrieval With Cascaded Neural Networks","created":"2021-06-18","cited_by_count":6,"area":null},{"id":"2212.13597","title":"Optimal Regularization for a Data Source","created":"2022-12-27","cited_by_count":6,"area":null},{"id":"2303.11217","title":"Inverse problem regularization with hierarchical variational autoencoders","created":"2023-03-20","cited_by_count":6,"area":null},{"id":"2402.05637","title":"Learning pseudo-contractive denoisers for inverse problems","created":"2024-02-08","cited_by_count":6,"area":null},{"id":"2404.03998","title":"PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images","created":"2024-04-05","cited_by_count":6,"area":null},{"id":"2502.04079","title":"DEALing with Image Reconstruction: Deep Attentive Least Squares","created":"2025-02-06","cited_by_count":6,"area":null},{"id":"2102.05105","title":"Deep learning architectural designs for super-resolution of noisy images","created":"2021-02-09","cited_by_count":5,"area":null},{"id":"2103.14249","title":"Marine Snow Removal Benchmarking Dataset","created":"2021-03-26","cited_by_count":5,"area":null},{"id":"2202.05269","title":"A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers","created":"2022-02-10","cited_by_count":5,"area":null},{"id":"2210.06330","title":"CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping","created":"2022-10-12","cited_by_count":5,"area":null},{"id":"2212.07786","title":"Convergent Data-driven Regularizations for CT Reconstruction","created":"2022-12-14","cited_by_count":5,"area":null},{"id":"2304.05589","title":"Discovering Structure From Corruption for Unsupervised Image Reconstruction","created":"2023-04-12","cited_by_count":5,"area":null},{"id":"2309.13584","title":"Solving Low-Dose CT Reconstruction via GAN with Local Coherence","created":"2023-09-24","cited_by_count":5,"area":null},{"id":"2311.01216","title":"Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter","created":"2023-11-02","cited_by_count":5,"area":null},{"id":"2412.11108","title":"Plug-and-Play Priors as a Score-Based Method","created":"2024-12-15","cited_by_count":5,"area":null},{"id":"2506.14605","title":"Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching","created":"2025-06-17","cited_by_count":5,"area":null},{"id":"2507.15397","title":"MAP Estimation with Denoisers: Convergence Rates and Guarantees","created":"2025-07-21","cited_by_count":5,"area":null},{"id":"2106.15327","title":"Patch-Based Image Restoration using Expectation Propagation","created":"2021-06-18","cited_by_count":4,"area":null},{"id":"2110.10093","title":"Stochastic Primal-Dual Deep Unrolling","created":"2021-10-19","cited_by_count":4,"area":null},{"id":"2205.15395","title":"A hybrid approach to seismic deblending: when physics meets self-supervision","created":"2022-05-30","cited_by_count":4,"area":null},{"id":"2207.08939","title":"Learning Sparsity-Promoting Regularizers using Bilevel Optimization","created":"2022-07-18","cited_by_count":4,"area":null},{"id":"2210.14974","title":"SINCO: A Novel structural regularizer for image compression using implicit neural representations","created":"2022-10-26","cited_by_count":4,"area":null},{"id":"2211.00531","title":"Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model","created":"2022-11-01","cited_by_count":4,"area":null},{"id":"2304.08342","title":"NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems","created":"2023-04-17","cited_by_count":4,"area":null},{"id":"2310.05812","title":"Provably Convergent Data-Driven Convex-Nonconvex Regularization","created":"2023-10-09","cited_by_count":4,"area":null},{"id":"2311.08972","title":"Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging","created":"2023-11-15","cited_by_count":4,"area":null},{"id":"2410.20646","title":"Injectivity capacity of ReLU gates","created":"2024-10-28","cited_by_count":4,"area":null},{"id":"2505.08909","title":"Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Inverse Problems","created":"2025-05-13","cited_by_count":4,"area":null},{"id":"2510.01608","title":"NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems","created":"2025-10-02","cited_by_count":4,"area":null},{"id":"2103.13909","title":"Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition","created":"2021-03-25","cited_by_count":3,"area":null}],"next_cursor":"100"}