FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representation

Haowen Zhou; Brandon Y. Feng; Steven S. Lin; Haiyun Guo; Mingshu Liang; Christopher A. Metzler; Changhuei Yang

doi:10.1117/12.3007004

13 March 2024 FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representation

Haowen Zhou, Brandon Y. Feng, Steven S. Lin, Haiyun Guo, Mingshu Liang, Christopher A. Metzler, Changhuei Yang

Proceedings Volume PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; PC128570W (2024) https://doi.org/10.1117/12.3007004
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Fourier ptychographic microscopy (FPM) has its strength in tackling the trade-off between resolution and field-of-view of imaging systems by computational methods. Here, we present a time-efficient and physics-based algorithm for FPM image stack reconstruction using implicit neural representation and tensor low-rank approximation. The method is free of any pre-training process and can be easily adapted to various computational microscopes. Compared to the conventional FPM methods for image stack reconstruction, the proposed method can be several times faster than conventional FPM methods on the same graphics processing units (GPU) and significantly reduce data volume for storage. The proposed method has potential applications in digital pathology and its downstream data-driven tasks, and can be beneficial to data collaboration in biological sciences.

Conference Presentation

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

Citation Download Citation

Haowen Zhou, Brandon Y. Feng, Steven S. Lin, Haiyun Guo, Mingshu Liang, Christopher A. Metzler, and Changhuei Yang "FPM-INR: Fourier ptychographic microscopy image stack reconstruction using implicit neural representation", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC128570W (13 March 2024); https://doi.org/10.1117/12.3007004

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KEYWORDS

Image restoration

Data storage

Microscopy

Data modeling

Biological samples

Data archive systems

Image processing

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