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Title: Distinguishing one from many using super-resolution compressive sensing

Abstract

We present that distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that the PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l 1-norm, l 0-norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sourcesmore » and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. In conclusion, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.« less

Authors:
 [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1441481
Report Number(s):
SAND-2018-2946J
662293
DOE Contract Number:
AC04-94AL85000; NA0003525
Resource Type:
Conference
Resource Relation:
Journal Volume: 10658; Conference: SPIE Defense and Commercial Sensing, Orlando, FL (United States), 15-19 Apr 2018
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; 42 ENGINEERING; compressed sensing; regularization; Rayleigh limit; point spread function; super-resolution

Citation Formats

Anthony, Stephen Michael, Mulcahy-Stanislawczyk, Johnathan, Shields, Eric A., and Woodbury, Drew P. Distinguishing one from many using super-resolution compressive sensing. United States: N. p., 2018. Web. doi:10.1117/12.2304476.
Anthony, Stephen Michael, Mulcahy-Stanislawczyk, Johnathan, Shields, Eric A., & Woodbury, Drew P. Distinguishing one from many using super-resolution compressive sensing. United States. doi:10.1117/12.2304476.
Anthony, Stephen Michael, Mulcahy-Stanislawczyk, Johnathan, Shields, Eric A., and Woodbury, Drew P. Mon . "Distinguishing one from many using super-resolution compressive sensing". United States. doi:10.1117/12.2304476.
@article{osti_1441481,
title = {Distinguishing one from many using super-resolution compressive sensing},
author = {Anthony, Stephen Michael and Mulcahy-Stanislawczyk, Johnathan and Shields, Eric A. and Woodbury, Drew P.},
abstractNote = {We present that distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that the PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l1-norm, l0-norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sources and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. In conclusion, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.},
doi = {10.1117/12.2304476},
journal = {},
number = ,
volume = 10658,
place = {United States},
year = {Mon May 14 00:00:00 EDT 2018},
month = {Mon May 14 00:00:00 EDT 2018}
}

Conference:
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