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Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm
Koki Yamada1, Natsuki Akaishi1, Kohei Yatabe1
1Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho, Koganei Tokyo Japan.
Journal of Applied Crystallography
|October 10, 2024
Summary
A new hybrid phase retrieval method combines deep neural networks (DNNs) with iterative algorithms for ptychography. This approach enhances image quality and robustness, even with limited data and low illumination.
Area of Science:
- Computational imaging
- X-ray microscopy
- Phase retrieval algorithms
Background:
- Ptychography is a powerful computational imaging technique essential for microscopic analysis.
- Phase retrieval algorithms are critical for ptychography's imaging quality.
- Deep neural network (DNN)-based methods offer improved phase retrieval but face limitations with experimental variability and data collection.
Purpose of the Study:
- To develop a robust ptychographic phase-retrieval algorithm overcoming DNN limitations.
- To enhance imaging quality and reduce computational demands in ptychography.
- To improve the adaptability of DNN-based methods to varying experimental conditions.
Main Methods:
- A hybrid approach combining model-based iterative algorithms (e.g., ePIE) with a DNN-based denoiser.
- Training the DNN denoiser using a formula-driven supervised approach with synthetic data, avoiding the need for measured specimen images.
- Evaluating the method using simulations of hard X-ray ptychography and real-world datasets.
Main Results:
- The proposed hybrid method reconstructs higher-spatial-resolution images compared to traditional ePIE and rPIE.
- Achieved comparable or superior image quality with half the number of iterations.
- Demonstrated robustness to hyperparameters and effectiveness with low illumination intensity data.
- Successfully reconstructed images from datasets with lower overlap ratios.
Conclusions:
- The hybrid DNN-iterative algorithm offers a significant advancement in ptychographic phase retrieval.
- This method enhances robustness, reduces data requirements, and improves imaging performance.
- It provides a more adaptable and efficient solution for various ptychographic applications, including those with challenging experimental conditions.
