121 research outputs found
Reconstruction from Periodic Nonlinearities, With Applications to HDR Imaging
We consider the problem of reconstructing signals and images from periodic
nonlinearities. For such problems, we design a measurement scheme that supports
efficient reconstruction; moreover, our method can be adapted to extend to
compressive sensing-based signal and image acquisition systems. Our techniques
can be potentially useful for reducing the measurement complexity of high
dynamic range (HDR) imaging systems, with little loss in reconstruction
quality. Several numerical experiments on real data demonstrate the
effectiveness of our approach
Deep learning in computational microscopy
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 on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio
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