Higher degree total variation (HDTV) algorithms for biomedical inverse problems

Yue Hu; Mathews Jacob

doi:10.1117/12.893062

Higher degree total variation (HDTV) algorithms for biomedical inverse problems

Yue Hu^*
, Mathews Jacob

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We introduce novel generalized regularization functionals to overcome the practical problems associated with current total variation (TV) penalty. Specically, we extend the TV scheme to higher order derivatives to improve the representation of smoothly varying image regions. In addition, we introduce a rotation invariant anisotropic TV penalty to improve the regularity of the edge contours. The validation of the scheme demonstrates the signicantly improved performance of the proposed methods in the context of compressed sensing and denoising.

Original language	English
Title of host publication	Wavelets and Sparsity XIV
DOIs	https://doi.org/10.1117/12.893062
State	Published - 2011
Externally published	Yes
Event	Wavelets and Sparsity XIV - San Diego, CA, United States Duration: 21 Aug 2011 → 24 Aug 2011

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	8138
ISSN (Print)	0277-786X

Conference

Conference	Wavelets and Sparsity XIV
Country/Territory	United States
City	San Diego, CA
Period	21/08/11 → 24/08/11

Keywords

Total variation (TV)
anisotropic
isotropic
majorize minimize (MM)
rotation invariant

Access to Document

10.1117/12.893062

Cite this

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title = "Higher degree total variation (HDTV) algorithms for biomedical inverse problems",

abstract = "We introduce novel generalized regularization functionals to overcome the practical problems associated with current total variation (TV) penalty. Specically, we extend the TV scheme to higher order derivatives to improve the representation of smoothly varying image regions. In addition, we introduce a rotation invariant anisotropic TV penalty to improve the regularity of the edge contours. The validation of the scheme demonstrates the signicantly improved performance of the proposed methods in the context of compressed sensing and denoising.",

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author = "Yue Hu and Mathews Jacob",

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AU - Jacob, Mathews

PY - 2011

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N2 - We introduce novel generalized regularization functionals to overcome the practical problems associated with current total variation (TV) penalty. Specically, we extend the TV scheme to higher order derivatives to improve the representation of smoothly varying image regions. In addition, we introduce a rotation invariant anisotropic TV penalty to improve the regularity of the edge contours. The validation of the scheme demonstrates the signicantly improved performance of the proposed methods in the context of compressed sensing and denoising.

AB - We introduce novel generalized regularization functionals to overcome the practical problems associated with current total variation (TV) penalty. Specically, we extend the TV scheme to higher order derivatives to improve the representation of smoothly varying image regions. In addition, we introduce a rotation invariant anisotropic TV penalty to improve the regularity of the edge contours. The validation of the scheme demonstrates the signicantly improved performance of the proposed methods in the context of compressed sensing and denoising.

KW - Total variation (TV)

KW - anisotropic

KW - isotropic

KW - majorize minimize (MM)

KW - rotation invariant

UR - https://www.scopus.com/pages/publications/80055033123

U2 - 10.1117/12.893062

DO - 10.1117/12.893062

M3 - 会议稿件

AN - SCOPUS:80055033123

SN - 9780819487483

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Wavelets and Sparsity XIV

T2 - Wavelets and Sparsity XIV

Y2 - 21 August 2011 through 24 August 2011

ER -

Higher degree total variation (HDTV) algorithms for biomedical inverse problems

Abstract

Publication series

Conference

Keywords

Access to Document

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