  Fourier Ptychographic Microscopy (FPM) is a computational imaging method that
is able to super-resolve features beyond the diffraction-limit set by the
objective lens of a traditional microscope. This is accomplished by using
synthetic aperture and phase retrieval algorithms to combine many measurements
captured by an LED array microscope with programmable source patterns. FPM
provides simultaneous large field-of-view and high resolution imaging, but at
the cost of reduced temporal resolution, thereby limiting live cell
applications. In this work, we learn LED source pattern designs that compress
the many required measurements into only a few, with negligible loss in
reconstruction quality or resolution. This is accomplished by recasting the
super-resolution reconstruction as a Physics-based Neural Network and learning
the experimental design to optimize the network's overall performance.
Specifically, we learn LED patterns for different applications (e.g. amplitude
contrast and quantitative phase imaging) and show that the designs we learn
through simulation generalize well in the experimental setting. Further, we
discuss a context-specific loss function, practical memory limitations, and
interpretability of our learned designs.
