Efficient Inversion of Multiple-Scattering Model for Optical Diffraction Tomography
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Abstract (arXiv)
Optical diffraction tomography relies on solving an inverse scattering problem governed by the wave equation. Classical reconstruction algorithms are based on linear approximations of the forward model (Born or Rytov), which limits their applicability to thin samples with low refractive-index contrasts. More recent works have shown the benefit of adopting nonlinear models. They account for multiple scattering and reflections, improving the quality of reconstruction. To reduce the complexity and memory requirements of these methods, we derive an explicit formula for the Jacobian matrix of the nonlinear Lippmann-Schwinger model which lends itself to an efficient evaluation of the gradient of the data- fidelity term. This allows us to deploy efficient methods to solve the corresponding inverse problem subject to sparsity constraints.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jul 2017.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 2.
6 papers.
- 2023Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layerscited by 11
- 2022Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priorscited by 9
- 2026Self-Supervised Learning from Noisy and Incomplete Datacited by 5
- 2025Neural Inverse Scattering with Score-based Regularizationcited by 1
- 2024Physics-guided Full Waveform Inversion using Encoder-Solver Convolutional Neural Networkscited by 0
- 2026ScoreField: Neural Inverse Scattering with Score-Based Generative Priorscited by 0
- 2026ScoreField: Neural Inverse Scattering with Score-Based Generative Priors3 Aug 2026
- 2026Self-Supervised Learning from Noisy and Incomplete Data6 Jan 2026
- 2025Neural Inverse Scattering with Score-based Regularization20 May 2025
- 2024Physics-guided Full Waveform Inversion using Encoder-Solver Convolutional Neural Networks27 May 2024
- 2023Multigrid-Augmented Deep Learning Preconditioners for the Helmholtz Equation using Compact Implicit Layers30 Jun 2023
- 2022Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors18 Mar 2022