60,956 research outputs found
Compressive Wavefront Sensing with Weak Values
We demonstrate a wavefront sensor based on the compressive sensing,
single-pixel camera. Using a high-resolution spatial light modulator (SLM) as a
variable waveplate, we weakly couple an optical field's transverse-position and
polarization degrees of freedom. By placing random, binary patterns on the SLM,
polarization serves as a meter for directly measuring random projections of the
real and imaginary components of the wavefront. Compressive sensing techniques
can then recover the wavefront. We acquire high quality, 256x256 pixel images
of the wavefront from only 10,000 projections. Photon-counting detectors give
sub-picowatt sensitivity
Fast Hadamard transforms for compressive sensing of joint systems: measurement of a 3.2 million-dimensional bi-photon probability distribution
We demonstrate how to efficiently implement extremely high-dimensional
compressive imaging of a bi-photon probability distribution. Our method uses
fast-Hadamard-transform Kronecker-based compressive sensing to acquire the
joint space distribution. We list, in detail, the operations necessary to
enable fast-transform-based matrix-vector operations in the joint space to
reconstruct a 16.8 million-dimensional image in less than 10 minutes. Within a
subspace of that image exists a 3.2 million-dimensional bi-photon probability
distribution. In addition, we demonstrate how the marginal distributions can
aid in the accuracy of joint space distribution reconstructions
Compressive ghost imaging
We describe an advanced image reconstruction algorithm for pseudothermal
ghost imaging, reducing the number of measurements required for image recovery
by an order of magnitude. The algorithm is based on compressed sensing, a
technique that enables the reconstruction of an N-pixel image from much less
than N measurements. We demonstrate the algorithm using experimental data from
a pseudothermal ghost-imaging setup. The algorithm can be applied to data taken
from past pseudothermal ghost-imaging experiments, improving the
reconstruction's quality.Comment: Comments are welcom
Building Proteins in a Day: Efficient 3D Molecular Reconstruction
Discovering the 3D atomic structure of molecules such as proteins and viruses
is a fundamental research problem in biology and medicine. Electron
Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure
estimation which attempts to reconstruct 3D structures from 2D images. This
paper addresses the challenging problem of 3D reconstruction from 2D Cryo-EM
images. A new framework for estimation is introduced which relies on modern
stochastic optimization techniques to scale to large datasets. We also
introduce a novel technique which reduces the cost of evaluating the objective
function during optimization by over five orders or magnitude. The net result
is an approach capable of estimating 3D molecular structure from large scale
datasets in about a day on a single workstation.Comment: To be presented at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
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