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, with simulated data, that the joint
optimization yields improved image reconstruction as compared with sole
optimization of the post-processing reconstruction algorithm
