We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging.Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based onlearning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to performFourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery withconsiderably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescentmicroscopic images from blurred images captured at overfocused or underfocused planes.
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