mlsanityData to 14 Oct 2026

DeepInverse: A Python package for solving imaging inverse problems with deep learning

arXiv 2505.20160 ↗First posted 26 May 2025eess.IVcited by 28

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Abstract (arXiv)

DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementation of forward operators (e.g., optics, MRI, tomography), to the definition and resolution of variational problems and the design and training of advanced neural network architectures. In this paper, we describe the main functionality of the library and discuss the main design choices.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 May 2025.

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Citing papers per year: 2025: 12; 2026: 16.

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  1. 2025Learning Regularization Functionals for Inverse Problems: A Comparative Studycited by 15
  2. 2025The Generation Phases of Flow Matching: a Denoising Perspectivecited by 7
  3. 2026Self-Supervised Learning from Noisy and Incomplete Datacited by 5
  4. 2026Geometry-Aware Discretization Error of Diffusion Modelscited by 3
  5. 2025MRpro: open framework for model-based, learned, and quantitative MR imagingcited by 2
  6. 2025LVTINO: LAtent Video consisTency INverse sOlver for High Definition Video Restorationcited by 2
  7. 2026A Stability Benchmark of Generative Regularizers for Inverse Problemscited by 2
  8. 2025A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAPcited by 1
  9. 2025Preconditioned Regularized Wasserstein Proximal Samplingcited by 1
  10. 2025Equivariant Splitting: Self-supervised learning from incomplete datacited by 1
  11. 2025Expressive Power of Implicit Models: Rich Equilibria and Test-Time Scalingcited by 1
  12. 2025Equivariant Deep Equilibrium Models for Imaging Inverse Problemscited by 1
16 more
  1. 2025CHEM: Estimating and Understanding Hallucinations in Deep Learning for Image Processingcited by 1
  2. 2026GSNR: Graph Smooth Null-Space Representation for Inverse Problemscited by 1
  3. 2026The Finite Primitive Basis Theorem for Computational Imaging: Formal Foundations of the OperatorGraph Representationcited by 1
  4. 2026Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoisingcited by 1
  5. 2026Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstructioncited by 1
  6. 2026Gradient Step Plug-and-Play Model for Dental Cone-Beam CT Reconstructioncited by 1
  7. 2026SP3: Spherical Priors for Plug-and-Play Restorationcited by 1
  8. 2025Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurementscited by 0
  9. 2025Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problemscited by 0
  10. 2026Scale Equivariance Regularization and Feature Lifting in High Dynamic Range Modulo Imagingcited by 0
  11. 2026Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truthcited by 0
  12. 2026UNet-AF: Alias-free UNet architecturescited by 0
  13. 2026Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstructioncited by 0
  14. 2026Hyperspectral Diffusion Equivariant Imaging (HyDiff-EI): A Self-supervised Framework for Hyperspectral Image Inpaintingcited by 0
  15. 2026Convergent Plug-and-Play Image Restoration with Annealed Noise Levelscited by 0
  16. 2026BAM! Bayesian Anything Model: a foundation model for generative computational imagingcited by 0