UNN-based self-calibration of lens-less ptychographic microscopy

Yewon Kim; Chulmin Joo; Hongseong Kim; Ingyoung Kim

doi:10.1117/12.3000381

13 March 2024 UNN-based self-calibration of lens-less ptychographic microscopy

Yewon Kim, Chulmin Joo, Hongseong Kim, Ingyoung Kim

Proceedings Volume 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; 128570B (2024) https://doi.org/10.1117/12.3000381
Event: SPIE BiOS, 2024, San Francisco, California, United States

Conference Poster

Abstract

On-chip lens-less ptychographic microscopy enables large field-of-view, high-resolution imaging of thin specimens by utilizing multiple intensity images acquired with multi-angle illumination and an iterative phase retrieval algorithm. Image reconstruction in lens-less ptychography, however, heavily relies on accurate forward model for image formation, and thus any discrepancies between forward model and experimental settings (e.g., mismatch in sample-to-detector distance or a slight tilt of object plane) would result in poor reconstruction image quality or inaccurate phase estimation. Here, we propose a deep learning-based autocalibration strategy for lens-less ptychographic microscopy, which does not require precise forward model and large training datasets. Our method is based on untrained neural network that incorporates parameterized physical forward model and system aberration.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yewon Kim, Chulmin Joo, Hongseong Kim, and Ingyoung Kim "UNN-based self-calibration of lens-less ptychographic microscopy", Proc. SPIE 12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, 128570B (13 March 2024); https://doi.org/10.1117/12.3000381

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