1,083 research outputs found
Coherence retrieval using trace regularization
The mutual intensity and its equivalent phase-space representations quantify
an optical field's state of coherence and are important tools in the study of
light propagation and dynamics, but they can only be estimated indirectly from
measurements through a process called coherence retrieval, otherwise known as
phase-space tomography. As practical considerations often rule out the
availability of a complete set of measurements, coherence retrieval is usually
a challenging high-dimensional ill-posed inverse problem. In this paper, we
propose a trace-regularized optimization model for coherence retrieval and a
provably-convergent adaptive accelerated proximal gradient algorithm for
solving the resulting problem. Applying our model and algorithm to both
simulated and experimental data, we demonstrate an improvement in
reconstruction quality over previous models as well as an increase in
convergence speed compared to existing first-order methods.Comment: 28 pages, 10 figures, accepted for publication in SIAM Journal on
Imaging Science
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
Noise Robustness of a Combined Phase Retrieval and Reconstruction Method for Phase-Contrast Tomography
Classical reconstruction methods for phase-contrast tomography consist of two
stages: phase retrieval and tomographic reconstruction. A novel algebraic
method combining the two was suggested by Kostenko et al. (Opt. Express, 21,
12185, 2013) and preliminary results demonstrating improved reconstruction
compared to a two-stage method given. Using simulated free-space propagation
experiments with a single sample-detector distance, we thoroughly compare the
novel method with the two-stage method to address limitations of the
preliminary results. We demonstrate that the novel method is substantially more
robust towards noise; our simulations point to a possible reduction in counting
times by an order of magnitude
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