Deep Hyperspectral Microscopy Based on Structured Light Sheet

We describe a computational hyperspectral microscope based on a structured light sheet that is generated using a digital micromirror device only. To reduce the acquisition time, we consider a small number of structured patterns. We solve the resulting inverse problem using an unrolled deep neural network, which limits the loss of spatial resolution.