6,801 research outputs found
Compressive Matched-Field Processing
Source localization by matched-field processing (MFP) generally involves
solving a number of computationally intensive partial differential equations.
This paper introduces a technique that mitigates this computational workload by
"compressing" these computations. Drawing on key concepts from the recently
developed field of compressed sensing, it shows how a low-dimensional proxy for
the Green's function can be constructed by backpropagating a small set of
random receiver vectors. Then, the source can be located by performing a number
of "short" correlations between this proxy and the projection of the recorded
acoustic data in the compressed space. Numerical experiments in a Pekeris ocean
waveguide are presented which demonstrate that this compressed version of MFP
is as effective as traditional MFP even when the compression is significant.
The results are particularly promising in the broadband regime where using as
few as two random backpropagations per frequency performs almost as well as the
traditional broadband MFP, but with the added benefit of generic applicability.
That is, the computationally intensive backpropagations may be computed offline
independently from the received signals, and may be reused to locate any source
within the search grid area
Phaseless computational imaging with a radiating metasurface
Computational imaging modalities support a simplification of the active
architectures required in an imaging system and these approaches have been
validated across the electromagnetic spectrum. Recent implementations have
utilized pseudo-orthogonal radiation patterns to illuminate an object of
interest---notably, frequency-diverse metasurfaces have been exploited as fast
and low-cost alternative to conventional coherent imaging systems. However,
accurately measuring the complex-valued signals in the frequency domain can be
burdensome, particularly for sub-centimeter wavelengths. Here, computational
imaging is studied under the relaxed constraint of intensity-only measurements.
A novel 3D imaging system is conceived based on 'phaseless' and compressed
measurements, with benefits from recent advances in the field of phase
retrieval. In this paper, the methodology associated with this novel principle
is described, studied, and experimentally demonstrated in the microwave range.
A comparison of the estimated images from both complex valued and phaseless
measurements are presented, verifying the fidelity of phaseless computational
imaging.Comment: 18 pages, 18 figures, articl
Sparse Representation of Astronomical Images
Sparse representation of astronomical images is discussed. It is shown that a
significant gain in sparsity is achieved when particular mixed dictionaries are
used for approximating these types of images with greedy selection strategies.
Experiments are conducted to confirm: i)Effectiveness at producing sparse
representations. ii)Competitiveness, with respect to the time required to
process large images.The latter is a consequence of the suitability of the
proposed dictionaries for approximating images in partitions of small
blocks.This feature makes it possible to apply the effective greedy selection
technique Orthogonal Matching Pursuit, up to some block size. For blocks
exceeding that size a refinement of the original Matching Pursuit approach is
considered. The resulting method is termed Self Projected Matching Pursuit,
because is shown to be effective for implementing, via Matching Pursuit itself,
the optional back-projection intermediate steps in that approach.Comment: Software to implement the approach is available on
http://www.nonlinear-approx.info/examples/node1.htm
Photon counting compressive depth mapping
We demonstrate a compressed sensing, photon counting lidar system based on
the single-pixel camera. Our technique recovers both depth and intensity maps
from a single under-sampled set of incoherent, linear projections of a scene of
interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional
reconstructions are required to image a three-dimensional scene. We demonstrate
intensity imaging and depth mapping at 256 x 256 pixel transverse resolution
with acquisition times as short as 3 seconds. We also show novelty filtering,
reconstructing only the difference between two instances of a scene. Finally,
we acquire 32 x 32 pixel real-time video for three-dimensional object tracking
at 14 frames-per-second.Comment: 16 pages, 8 figure
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