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Bole Ma, Chuxuan Huang, Sibing Hou

|October 13, 2023

Summary

Deep learning for complex wavefront retrieval (CWR) often crops data, reducing resolution. Our new IPMnet uses full diffraction patterns for higher resolution CWR, balancing computational cost and data quality.

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Area of Science:

Optics and Photonics
Computational Imaging
Artificial Intelligence

Background:

Deep learning (DL) shows promise for complex wavefront retrieval (CWR).
Current DL methods for CWR crop diffraction patterns, sacrificing space-bandwidth product (SBP) for computational efficiency.
This cropping limits the achievable resolution and field of view in wavefront reconstruction.

Purpose of the Study:

To develop a DL-based CWR method that overcomes the limitations of cropped diffraction patterns.
To propose an Imaging Process Matched Neural Network (IPMnet) that preserves the full SBP of diffraction data.
To achieve higher resolution and a larger field of view in complex wavefront retrieval.

Main Methods:

Developed an Imaging Process Matched Neural Network (IPMnet).
IPMnet is designed to process full-size diffraction patterns, maintaining a large SBP.
The network architecture is tailored to align with the physical diffraction process.

Main Results:

IPMnet successfully retrieves complex wavefronts from full-size diffraction patterns.
The method demonstrates improved resolution and a larger field of view compared to cropped-input methods.
Effectiveness was validated through both numerical simulations and experimental data.

Conclusions:

IPMnet offers a solution to the trade-off between computational resources and SBP in DL-based CWR.
The proposed method enhances the performance of complex wavefront retrieval by utilizing the complete diffraction information.
IPMnet advances the capabilities of DL for high-fidelity wavefront reconstruction.