{"total":22,"rows":[{"id":"2510.01755","title":"Learning Regularization Functionals for Inverse Problems: A Comparative Study","created":"2025-10-02","cited_by_count":15,"area":null},{"id":"2601.03244","title":"Self-Supervised Learning from Noisy and Incomplete Data","created":"2026-01-06","cited_by_count":5,"area":null},{"id":"2507.23129","title":"MRpro: open framework for model-based, learned, and quantitative MR imaging","created":"2025-07-30","cited_by_count":2,"area":null},{"id":"2605.10076","title":"A Stability Benchmark of Generative Regularizers for Inverse Problems","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2508.02923","title":"A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAP","created":"2025-08-04","cited_by_count":1,"area":null},{"id":"2509.01685","title":"Preconditioned Regularized Wasserstein Proximal Sampling","created":"2025-09-01","cited_by_count":1,"area":null},{"id":"2510.00929","title":"Equivariant Splitting: Self-supervised learning from incomplete data","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2511.18667","title":"Equivariant Deep Equilibrium Models for Imaging Inverse Problems","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2512.09806","title":"CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processing","created":"2025-12-10","cited_by_count":1,"area":null},{"id":"2602.20328","title":"GSNR: Graph Smooth Null-Space Representation for Inverse Problems","created":"2026-02-23","cited_by_count":1,"area":null},{"id":"2602.20550","title":"The Finite Primitive Basis Theorem for Computational Imaging: Formal Foundations of the OperatorGraph Representation","created":"2026-02-24","cited_by_count":1,"area":null},{"id":"2603.25869","title":"Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising","created":"2026-03-26","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":"2605.28124","title":"Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstruction","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2606.16396","title":"SP$^3$: Spherical Priors for Plug-and-Play Restoration","created":"2026-06-15","cited_by_count":1,"area":null},{"id":"2510.27663","title":"Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurements","created":"2025-10-31","cited_by_count":0,"area":null},{"id":"2511.12257","title":"Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problems","created":"2025-11-15","cited_by_count":0,"area":null},{"id":"2601.23037","title":"Scale Equivariance Regularization and Feature Lifting in High Dynamic Range Modulo Imaging","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2603.01332","title":"Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truth","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2606.24567","title":"Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2609.32393","title":"Convergent Plug-and-Play Image Restoration with Annealed Noise Levels","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.39660","title":"BAM! Bayesian Anything Model: a foundation model for generative computational imaging","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}