{"total":53,"rows":[{"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":"2211.12461","title":"A Neural-Network-Based Convex Regularizer for Inverse Problems","created":"2022-11-22","cited_by_count":41,"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":"2308.10542","title":"Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms","created":"2023-08-21","cited_by_count":31,"area":null},{"id":"2102.06665","title":"Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction","created":"2021-02-12","cited_by_count":30,"area":null},{"id":"2106.06513","title":"Learning the optimal Tikhonov regularizer for inverse problems","created":"2021-06-11","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":"2103.10182","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":"2111.12506","title":"Generalized Normalizing Flows via Markov Chains","created":"2021-11-24","cited_by_count":23,"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":"2210.13834","title":"Stable Deep MRI Reconstruction using Generative Priors","created":"2022-10-25","cited_by_count":21,"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":"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":"2108.06073","title":"Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion","created":"2021-08-13","cited_by_count":11,"area":null},{"id":"2107.14742","title":"Connections between Numerical Algorithms for PDEs and Neural Networks","created":"2021-07-30","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":"2308.02631","title":"Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction","created":"2023-08-04","cited_by_count":10,"area":null},{"id":"2203.12658","title":"Computed Tomography Reconstruction using Generative Energy-Based Priors","created":"2022-03-23","cited_by_count":9,"area":null},{"id":"2110.12520","title":"Learning convex regularizers satisfying the variational source condition for inverse problems","created":"2021-10-24","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":"2402.17951","title":"QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction","created":"2024-02-28","cited_by_count":6,"area":null},{"id":"2502.01358","title":"Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper]","created":"2025-02-03","cited_by_count":6,"area":null},{"id":"2103.16151","title":"Adversarially learned iterative reconstruction for imaging inverse problems","created":"2021-03-30","cited_by_count":5,"area":null},{"id":"2211.13452","title":"Deep unfolding as iterative regularization for imaging inverse problems","created":"2022-11-24","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":"2309.13584","title":"Solving Low-Dose CT Reconstruction via GAN with Local Coherence","created":"2023-09-24","cited_by_count":5,"area":null},{"id":"2406.12289","title":"Stability of Data-Dependent Ridge-Regularization for Inverse Problems","created":"2024-06-18","cited_by_count":5,"area":null},{"id":"2505.06549","title":"Good Things Come in Pairs: Paired Autoencoders for Inverse Problems","created":"2025-05-10","cited_by_count":5,"area":null},{"id":"2208.03107","title":"Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions","created":"2022-08-05","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":"2108.13993","title":"Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods","created":"2021-08-31","cited_by_count":3,"area":null},{"id":"2209.12651","title":"Learning Variational Models with Unrolling and Bilevel Optimization","created":"2022-09-26","cited_by_count":3,"area":null},{"id":"2404.05445","title":"Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation","created":"2024-04-08","cited_by_count":3,"area":null},{"id":"2407.06608","title":"Iteratively Refined Image Reconstruction with Learned Attentive Regularizers","created":"2024-07-09","cited_by_count":3,"area":null},{"id":"2408.16852","title":"The Star Geometry of Critic-Based Regularizer Learning","created":"2024-08-29","cited_by_count":3,"area":null},{"id":"2410.12441","title":"A primal-dual algorithm for image reconstruction with input-convex neural network regularizers","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2112.00913","title":"CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing","created":"2021-12-02","cited_by_count":2,"area":null},{"id":"2508.06490","title":"Multivariate Fields of Experts for Convergent Image Reconstruction","created":"2025-08-08","cited_by_count":2,"area":null},{"id":"2108.11730","title":"Deep learning based dictionary learning and tomographic image reconstruction","created":"2021-08-26","cited_by_count":1,"area":null},{"id":"2112.07990","title":"Supervised learning of analysis-sparsity priors with automatic differentiation","created":"2021-12-15","cited_by_count":1,"area":null},{"id":"2203.10640","title":"Multimodal learning-based inversion models for the space-time reconstruction of satellite-derived geophysical fields","created":"2022-03-20","cited_by_count":1,"area":null},{"id":"2204.11146","title":"Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Prior","created":"2022-04-23","cited_by_count":1,"area":null},{"id":"2301.10260","title":"Learned Interferometric Imaging for the SPIDER Instrument","created":"2023-01-24","cited_by_count":1,"area":null},{"id":"2312.14078","title":"Learned reconstruction methods for inverse problems: sample error estimates","created":"2023-12-21","cited_by_count":1,"area":null},{"id":"2402.12072","title":"Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview","created":"2024-02-19","cited_by_count":1,"area":null},{"id":"2508.19068","title":"Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2602.20921","title":"On the Generalization Behavior of Deep Residual Networks From a Dynamical System Perspective","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2603.27158","title":"Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstruction","created":"2026-03-28","cited_by_count":1,"area":null},{"id":"2110.02813","title":"Accelerated First Order Methods for Variational Imaging","created":"2021-10-06","cited_by_count":0,"area":null},{"id":"2210.10050","title":"Explainable bilevel optimization: an application to the Helsinki deblur challenge","created":"2022-10-18","cited_by_count":0,"area":null},{"id":"2308.16612","title":"Neural Gradient Regularizer","created":"2023-08-31","cited_by_count":0,"area":null},{"id":"2604.24347","title":"Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2606.30230","title":"A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems","created":"2026-06-29","cited_by_count":0,"area":null}],"next_cursor":null}