This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms-Theory and Practice

Observations in many applications consist of counts of discrete events, such as photons hitting a detector, which cannot be effectively modeled using an additive bounded or Gaussian noise model, and instead require a Poisson noise model. As a result, accurate reconstruction of a spatially or tempora...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 21; no. 3; pp. 1084 - 1096
Main Authors:	Harmany, Z. T., Marcia, R. F., Willett, R. M.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.03.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Applied sciences Approximation Approximation methods Compressed sensing (CS) Convergence convex optimization Detectors Estimation Exact sciences and technology Image processing Image reconstruction Information, signal and communications theory Inverse problems Mathematical analysis Mathematical models Minimization multiscale Noise photon-limited imaging Poisson noise Reconstruction Sampling, quantization Signal and communications theory Signal processing sparse approximation Studies Telecommunications and information theory total variation (TV) wavelets Iterative method photon-limited imaging Poisson process Convex programming Poisson noise Optimization Gaussian noise Least squares method Imaging Quadratic approximation Sparse representation Likelihood function Discrete event system Photon counting Logarithmic function Poisson distribution Algorithm Photodetector Inverse problem Gaussian process Television Multiscale method Compressed sensing (CS) total variation (TV) wavelets sparse approximation convex optimization Objective function multiscale Compressed sensing
ISSN:	1057-7149, 1941-0042, 1941-0042
Online Access:	Get full text
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