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Optimal physical preprocessing for example-based super-resolution (Conference Presentation)
Author(s): Vidya Ganapati

Paper Abstract

In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. This data-driven approach to solving the inverse problem of increasing image resolution has been implemented with deep learning algorithms. In this work, we explore modifying the imaging hardware in order to collect more informative low-resolution images for better ultimate high-resolution image reconstruction. We show that this "physical preprocessing" allows for improved image reconstruction with deep learning in Fourier ptychographic microscopy. Fourier ptychographic microscopy is a technique allowing for both high resolution and high field-of-view at the cost of temporal resolution. In Fourier ptychographic microscopy, variable illumination patterns are used to collect multiple low-resolution images. These low-resolution images are then computationally combined to create an image with resolution exceeding that of any single image from the microscope. We use deep learning to jointly optimize the illumination pattern with the post-processing reconstruction algorithm for a given sample type, allowing for single-shot imaging with both high resolution and high field-of-view. We demonstrate that the joint optimization yields improved image reconstruction as compared with sole optimization of the post-processing reconstruction algorithm.

Paper Details

Date Published: 4 March 2019
Proc. SPIE 10937, Optical Data Science II, 109370D (4 March 2019); doi: 10.1117/12.2506636
Show Author Affiliations
Vidya Ganapati, Swarthmore College (United States)

Published in SPIE Proceedings Vol. 10937:
Optical Data Science II
Bahram Jalali; Ken-ichi Kitayama, Editor(s)

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