mlsanityData to 14 Oct 2026

Neural Inverse Scattering with Score-based Regularization

arXiv 2505.14560 ↗First posted 20 May 2025eess.IV cs.CVcited by 1

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Abstract (arXiv)

Inverse scattering is a fundamental challenge in many imaging applications, ranging from microscopy to remote sensing. Solving this problem often requires jointly estimating two unknowns -- the image and the scattering field inside the object -- necessitating effective image prior to regularize the inference. In this paper, we propose a regularized neural field (NF) approach which integrates the denoising score function used in score-based generative models. The neural field formulation offers convenient flexibility to performing joint estimation, while the denoising score function imposes the rich structural prior of images. Our results on three high-contrast simulated objects show that the proposed approach yields a better imaging quality compared to the state-of-the-art NF approach, where regularization is based on total variation.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

11 of 23 references.

References · 11

  1. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  2. 2021Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fieldscited by 71
  3. 2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
  4. 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48
  5. 2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
  6. 2018Efficient and accurate inversion of multiple scattering with deep learningcited by 10
  7. 2019High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity imagescited by 9
  8. 2024Imaging Interiors: An Implicit Solution to Electromagnetic Inverse Scattering Problemscited by 8
  9. 2017Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomographycited by 6
  10. 2017SEAGLE: Sparsity-Driven Image Reconstruction under Multiple Scatteringcited by 5
  11. 2017Accelerated Image Reconstruction for Nonlinear Diffractive Imagingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026ScoreField: Neural Inverse Scattering with Score-Based Generative Priorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data
  2. 2025Diff-Unfolding: A Model-Based Score Learning Framework for Inverse Problems
  3. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imaging
  4. 2023Neural-network-based regularization methods for inverse problems in imaging
  5. 2026Learning Normalized Energy Models for Linear Inverse Problems
  6. 2024Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
  7. 2023SUD2: Supervision by Denoising Diffusion Models for Image Reconstruction
  8. 2025Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models