mlsanityData to 7 Oct 2026

Neural-network-based regularization methods for inverse problems in imaging

arXiv 2312.14849 ↗First posted 22 Dec 2023math.OC eess.IVcited by 14

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Abstract (arXiv)

This review provides an introduction to - and overview of - the current state of the art in neural-network based regularization methods for inverse problems in imaging. It aims to introduce readers with a solid knowledge in applied mathematics and a basic understanding of neural networks to different concepts of applying neural networks for regularizing inverse problems in imaging. Distinguishing features of this review are, among others, an easily accessible introduction to learned generators and learned priors, in particular diffusion models, for inverse problems, and a section focusing explicitly on existing results in function space analysis of neural-network-based approaches in this context.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2023.

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Citing papers per year: 2025: 9; 2026: 5.

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  1. 2025Learning Regularization Functionals for Inverse Problems: A Comparative Studycited by 12
  2. 2025Good Things Come in Pairs: Paired Autoencoders for Inverse Problemscited by 5
  3. 2025Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstructioncited by 2
  4. 2025An invertible generative model for forward and inverse problemscited by 1
  5. 2025Graph Neural Regularizers for PDE Inverse Problemscited by 1
  6. 2026Weakly Convex Ridge Regularization for 3D Non-Cartesian MRI Reconstructioncited by 1
  7. 2026A Stability Benchmark of Generative Regularizers for Inverse Problemscited by 1
  8. 2025Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imagingcited by 0
  9. 2025Supporting renewable energy planning and operation with data-driven high-resolution ensemble weather forecastcited by 0
  10. 2025Deep regularization networks for inverse problems with noisy operatorscited by 0
  11. 2025DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstructioncited by 0
  12. 2026Neural Fields for NV-Center Inverse Sensingcited by 0
2 more
  1. 2026A Distributionally Robust Framework for Learned Reconstructions in Inverse Problemscited by 0
  2. 2026Learning spatially varying regularisation parameters of low regularity for image reconstructioncited by 0

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  1. 2025Diffusion models for inverse problems
  2. 2024A Survey on Diffusion Models for Inverse Problems
  3. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
  4. 2025Neural Inverse Scattering with Score-based Regularization
  5. 2025NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems
  6. 2026Learning Normalized Energy Models for Linear Inverse Problems
  7. 2024Blind Inversion using Latent Diffusion Priors
  8. 2023Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency