Data-driven experimental design for computational imaging (Conference Presentation)

Computational illumination microscopy has enabled imaging of a sample’s phase, spatial features beyond the diffraction limit (Fourier Ptychography), and 3D refractive index from intensity-based measurements captured on an LED array microscope. However, these methods require up to hundreds of images, limiting applications, particularly live sample imaging. Here, we demonstrate how the experimental design of a computational microscope can be optimized using data-driven methods to learn a compressed set of measurements, thereby improving the temporal resolution of the system. Specifically, we consider the image reconstruction as a physics-based network and learn the experimental design to optimize the system’s overall performance for a desired temporal resolution. Finally, we will discuss how the system’s experimental design can be learned on synthetic training data.