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SUMMARY:Sparse Recovery Algorithms for 3D Imaging using Point Spread Funct
 ion Engineering - Robert Plemmons (Wake Forest University)
DTSTART:20171103T140000Z
DTEND:20171103T145000Z
UID:TALK94432@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Co-authors: Chao Wang (Mathematics\, Chinese University of Hon
 g Kong)\, Raymond Chan (Mathematics\, Chinese University of Hong Kong)\, S
 udhakar Prasad (Physics\, University of New Mexico) <br><br>Imaging and lo
 calizing point sources with high accuracy in a 3D volume is an important b
 ut challenging task. For example\, super-resolution 3D single molecule loc
 alization is an area of intense interest in biology (cell imaging\, foldin
 g\, membrane behavior\, etc.)\, in chemistry (spectral diffusion\, molecul
 ar distortions\, etc.)\, and in physics (structures of materials\, quantum
  optics\, etc.).  We consider here the high-resolution imaging problem of 
 3D point source image recovery from 2D data using methods based on point s
 pread function (PSF) design. The methods involve a new technique\, recentl
 y patented by S. Prasad\, for applying rotating point spread functions wit
 h a single lobe to obtain depth from defocus. The amount of rotation of th
 e PSF encodes the depth position of the point source. The distribution of 
 point sources is discretized on  a cubical lattice where the indexes of no
 nzero entries represent the 3D locations of point sources. The values of t
 hese entries are the point source fluxes.  Finding the locations and fluxe
 s is a large-scale sparse 3D inverse problem and we have developed solutio
 n algorithms based on sparse recovery using non-convex optimization. Appli
 cations to high-resolution single molecule localization microscopy are des
 cribed\, as well as localization of space debris using a space-based teles
 cope.  Sparse recovery optimization methods\, including the Continuous Exa
 ct L0 (CEL0) algorithm\, are used in our numerical experiments.
LOCATION:Seminar Room 1\, Newton Institute
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