A subband adaptive iterative shrinkage/thresholding algorithm

Ilker Bayram; Ivan W. Selesnick

doi:10.1109/TSP.2009.2036064

A subband adaptive iterative shrinkage/thresholding algorithm

Ilker Bayram, Ivan W. Selesnick

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

We investigate a subband adaptive version of the popular iterative shrinkage/thresholding algorithm that takes different update steps and thresholds for each subband. In particular, we provide a condition that ensures convergence and discuss why making the algorithm subband adaptive accelerates the convergence. We also give an algorithm to select appropriate update steps and thresholds for when the distortion operator is linear and time invariant. The results in this paper may be regarded as extensions of the recent work by Vonesch and Unser.

Original language	English (US)
Pages (from-to)	1131-1143
Number of pages	13
Journal	IEEE Transactions on Signal Processing
Volume	58
Issue number	3 PART 1
DOIs	https://doi.org/10.1109/TSP.2009.2036064
State	Published - Mar 2010

Keywords

Deconvolution
Fast algorithm
Shrinkage
Subband adaptive
Thresholding
Wavelet regularized inverse problem

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/TSP.2009.2036064

Cite this

@article{0824cb22950b4ebb9c9cd0f31ae5fba5,

title = "A subband adaptive iterative shrinkage/thresholding algorithm",

abstract = "We investigate a subband adaptive version of the popular iterative shrinkage/thresholding algorithm that takes different update steps and thresholds for each subband. In particular, we provide a condition that ensures convergence and discuss why making the algorithm subband adaptive accelerates the convergence. We also give an algorithm to select appropriate update steps and thresholds for when the distortion operator is linear and time invariant. The results in this paper may be regarded as extensions of the recent work by Vonesch and Unser.",

keywords = "Deconvolution, Fast algorithm, Shrinkage, Subband adaptive, Thresholding, Wavelet regularized inverse problem",

author = "Ilker Bayram and Selesnick, {Ivan W.}",

year = "2010",

month = mar,

doi = "10.1109/TSP.2009.2036064",

language = "English (US)",

volume = "58",

pages = "1131--1143",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "3 PART 1",

}

TY - JOUR

T1 - A subband adaptive iterative shrinkage/thresholding algorithm

AU - Bayram, Ilker

AU - Selesnick, Ivan W.

PY - 2010/3

Y1 - 2010/3

N2 - We investigate a subband adaptive version of the popular iterative shrinkage/thresholding algorithm that takes different update steps and thresholds for each subband. In particular, we provide a condition that ensures convergence and discuss why making the algorithm subband adaptive accelerates the convergence. We also give an algorithm to select appropriate update steps and thresholds for when the distortion operator is linear and time invariant. The results in this paper may be regarded as extensions of the recent work by Vonesch and Unser.

AB - We investigate a subband adaptive version of the popular iterative shrinkage/thresholding algorithm that takes different update steps and thresholds for each subband. In particular, we provide a condition that ensures convergence and discuss why making the algorithm subband adaptive accelerates the convergence. We also give an algorithm to select appropriate update steps and thresholds for when the distortion operator is linear and time invariant. The results in this paper may be regarded as extensions of the recent work by Vonesch and Unser.

KW - Deconvolution

KW - Fast algorithm

KW - Shrinkage

KW - Subband adaptive

KW - Thresholding

KW - Wavelet regularized inverse problem

UR - http://www.scopus.com/inward/record.url?scp=78651106641&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78651106641&partnerID=8YFLogxK

U2 - 10.1109/TSP.2009.2036064

DO - 10.1109/TSP.2009.2036064

M3 - Article

AN - SCOPUS:78651106641

SN - 1053-587X

VL - 58

SP - 1131

EP - 1143

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

IS - 3 PART 1

ER -

A subband adaptive iterative shrinkage/thresholding algorithm

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this