Physics-based learning for measurement diversity in 3D refractive index microscopy (Conference Presentation)

3D refractive index imaging methods usually rely on a weak-scattering approximation that does not allow for thick samples to be imaged accurately. Recent methods such as 3D Fourier ptychographic microscopy (FPM) instead use multiple-scattering models which allow for thicker objects to be imaged. In practice the illumination-side coding of 3D FPM requires redundant information and may produce inaccurate reconstructions for thick samples. Here, we propose augmenting 3D FPM with detection-side coding using a spatial light modulator (SLM) and optimize the SLM coding strategy with physics-based machine learned pupil coding designs that are optimized for 3D reconstructions. We compare our learned designs to random-, defocus-, Zernike aberrations-based pupil codes in simulated and experimental results.