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

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arxiv 2505.20160 v2 pith:GO54BWS2 submitted 2025-05-26 eess.IV

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keywords libraryproblemsdeepinversedesignimaginginversemainsolving
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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.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    First-order asymptotic expansions of weak and Fréchet discretization errors in diffusion sampling are derived, explicit under Gaussian data through covariance geometry and robust to other data geometries.

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    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    P-Flow stabilizes flow-matching models for inverse problems via proxy gradients and Gaussian spherical projections, avoiding long-chain differentiation while maintaining prior consistency.

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  4. A Morse-Bott Framework for Blind Inverse Problems: Local Recovery Guarantees and the Failure of the MAP

    cs.CV 2025-08 conditional novelty 7.0 of 10

    MAP blind deconvolution fails with diffusion priors because blurry images are more likely, while true solutions persist only as local minima reachable with good initialization.

  5. Deep Scene-Driven Ordering of Hadamard Basis for Single-Pixel Spectral Imaging

    eess.IV 2026-07 conditional novelty 6.0 of 10

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  6. PnP-IPA: A Provably Convergent Plug-and-Play Inexact Proximal Algorithm for Nonconvex Imaging Problems

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    An inexact proximal PnP algorithm with adaptive line search converges globally for nonconvex imaging objectives without any bound on the regularization parameter.

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    cs.LG 2026-05 conditional novelty 6.0 of 10

    P-Flow solves linear inverse problems by optimizing the flow's source latent with a proxy gradient and a Gaussian-sphere projection, matching or beating prior restoration methods at far lower cost.

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    cs.LG 2026-05 unverdicted novelty 5.0 of 10

    P-Flow replaces direct differentiation through long flow paths with a proxy gradient update plus Gaussian spherical projection to stabilize reconstruction in linear inverse problems.

  10. Stabilizing RED using the Koopman Operator

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    A Koopman-operator-based monitor shrinks the RED step size whenever a low-dimensional model of the iterates predicts divergence, preventing PSNR collapse.

  11. Learned iterative networks: An operator learning perspective

    eess.IV 2025-12 conditional novelty 3.0 of 10

    Learned iterative reconstruction networks can be uniformly described as operator learning: the unrolled architecture fixes how to compute while the loss and data fix what to compute; for nonlinear inverse problems the...

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