Iterative phase retrieval in coherent diffractive imaging: practical issues
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Abstract (arXiv)
In this work, issues in phase retrieval in the coherent diffractive imaging (CDI) technique, from discussion on parameters for setting up a CDI experiment to evaluation of the goodness of the final reconstruction, are discussed. The distribution of objects under study by CDI often cannot be cross-validated by another imaging technique. It is therefore important to make sure that the developed CDI procedure delivers an artifact-free object reconstruction. Critical issues that can lead to artifacts are presented and recipes on how to avoid them are provided.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Sep 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.
3 papers.
- 2023Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Priorcited by 1
- 2024Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural networkcited by 1
- 2021Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functionscited by 0
- 2024Single-shot reconstruction of three-dimensional morphology of biological cells in digital holographic microscopy using a physics-driven neural network30 Sep 2024
- 2023Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior12 May 2023
- 2021Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions26 Nov 2021