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Part 1: Document Description
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Citation |
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Title: |
Supplementary data for "Object detection neural network improves Fourier ptychography reconstruction" |
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Identification Number: |
doi:10.18710/BBU6JD |
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Distributor: |
DataverseNO |
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Date of Distribution: |
2022-11-16 |
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Version: |
1 |
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Bibliographic Citation: |
Ströhl, Florian; Jadhav, Suyog S.; Ahluwalia, Balpreet Singh; Agarwal, Krishna; Prasad, Dilip K., 2022, "Supplementary data for "Object detection neural network improves Fourier ptychography reconstruction"", https://doi.org/10.18710/BBU6JD, DataverseNO, V1 |
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Citation |
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Title: |
Supplementary data for "Object detection neural network improves Fourier ptychography reconstruction" |
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Identification Number: |
doi:10.18710/BBU6JD |
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Authoring Entity: |
Ströhl, Florian (UiT The Arctic University of Norway) |
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Jadhav, Suyog S. (UiT The Arctic University of Norway) |
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Ahluwalia, Balpreet Singh (UiT The Arctic University of Norway) |
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Agarwal, Krishna (UiT The Arctic University of Norway) |
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Prasad, Dilip K. (UiT The Arctic University of Norway) |
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Other identifications and acknowledgements: |
Ströhl, Florian |
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Other identifications and acknowledgements: |
Jadhav, Suyog S. |
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Other identifications and acknowledgements: |
Ahluwalia, Balpreet Singh |
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Other identifications and acknowledgements: |
Agarwal, Krishna |
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Other identifications and acknowledgements: |
Prasad, Dilip K. |
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Producer: |
UiT The Arctic University of Norway |
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Date of Production: |
2020-09-08 |
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Software used in Production: |
Detectron2 |
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Software used in Production: |
PyTorch |
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Grant Number: |
285571 |
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Grant Number: |
336716 |
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Grant Number: |
804233 |
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Grant Number: |
836355 |
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Distributor: |
DataverseNO |
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Distributor: |
UiT The Arctic University of Norway |
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Access Authority: |
Jadhav, Suyog S. |
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Depositor: |
Jadhav, Suyog S. |
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Date of Deposit: |
2022-11-03 |
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Holdings Information: |
https://doi.org/10.18710/BBU6JD |
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Study Scope |
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Keywords: |
Computer and Information Science, Physics, Fourier Ptychography, Object Detection, Super Resolution Microscopy |
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Abstract: |
This dataset holds the trained deep learning models for our paper "Object detection neural network improves Fourier ptychography reconstruction". The results produced in the paper can be replicated through the use of these models in conjunction with the inference scripts provided in our GitHub repository: <a href="https://github.com/IAmSuyogJadhav/NN-Illumination-Estimation-FPM">External Link</a>. |
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<br /><b>Abstract</b> <br /> High resolution microscopy is heavily dependent on superb optical elements and superresolution microscopy even more so. Correcting unavoidable optical aberrations during post-processing is an elegant method to reduce the optical system’s complexity. A prime method that promises superresolution, aberration correction, and quantitative phase imaging is Fourier ptychography. This microscopy technique combines many images of the sample, recorded at differing illumination angles akin to computed tomography and uses error minimisation between the recorded images with those generated by a forward model. The more precise knowledge of those illumination angles is available for the image formation forward model, the better the result. Therefore, illumination estimation from the raw data is an important step and supports correct phase recovery and aberration correction. Here, we derive how illumination estimation can be cast as an object detection problem that permits the use of a fast convolutional neural network (CNN) for this task. We find that faster-RCNN delivers highly robust results and outperforms classical approaches by far with an up to 3-fold reduction in estimation errors. Intriguingly, we find that conventionally beneficial smoothing and filtering of raw data is counterproductive in this type of application. We present a detailed analysis of the network’s performance and provide all our developed software openly. |
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Date of Collection: |
2020-06-01-2020-09-08 |
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Kind of Data: |
Deep Learning Models |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Materials |
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GitHub repository: <a href="https://github.com/IAmSuyogJadhav/NN-Illumination-Estimation-FPM">https://github.com/IAmSuyogJadhav/NN-Illumination-Estimation-FPM</a> |
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Related Publications |
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Citation |
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Title: |
Ströhl, F., Jadhav, S., Ahluwalia, B.S., Agarwal, K. and Prasad, D.K., 2020. Object detection neural network improves Fourier ptychography reconstruction. Optics Express, 28(25), pp.37199-37208. |
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Identification Number: |
10.1364/OE.409679 |
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Bibliographic Citation: |
Ströhl, F., Jadhav, S., Ahluwalia, B.S., Agarwal, K. and Prasad, D.K., 2020. Object detection neural network improves Fourier ptychography reconstruction. Optics Express, 28(25), pp.37199-37208. |
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