Plug-and-Play Half-Quadratic Splitting for Ptychography
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Abstract (arXiv)
Ptychography is a coherent diffraction imaging method that uses phase retrieval techniques to reconstruct complex-valued images. It achieves this by sequentially illuminating overlapping regions of a sample with a coherent beam and recording the diffraction pattern. Although this addresses traditional imaging system challenges, it is computationally intensive and highly sensitive to noise, especially with reduced illumination overlap. Data-driven regularisation techniques have been applied in phase retrieval to improve reconstruction quality. In particular, plug-and-play (PnP) offers flexibility by integrating data-driven denoisers as implicit priors. In this work, we propose a half-quadratic splitting framework for using PnP and other data-driven priors for ptychography. We evaluate our method both on natural images and real test objects to validate its effectiveness for ptychographic image reconstruction.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 41 references that are in the map. First posted on arXiv 3 Dec 2024.
Builds on
7 of 41 references.
References · 7
- 2022Diffusion models as plug-and-play priorscited by 230
- 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 197
- 2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
- 2024PnP-Flow: Plug-and-Play Image Restoration with Flow Matchingcited by 92
- 2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
- 2018prDeep: Robust Phase Retrieval with a Flexible Deep Networkcited by 46
- 2024Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximationcited by 2
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inferencecited by 1
- 2025Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizerscited by 0
- 2026Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstructioncited by 0
- 2026Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction1 May 2026
- 2025Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference28 Sep 2025
- 2025Breaking the Weak Recovery Limit in Random Phase Retrieval with Learned Regularizers18 Sep 2025