Novel information processing for image de-noising based on sparse basis

Sheikh Md Rabiul Islam, Xu Huang, Keng-Liang Ou, Raul Fernandez Rojas, Hongyan Cui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality valuation indexes.


Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages443-451
Number of pages9
Volume9491
ISBN (Print)9783319265544
DOIs
Publication statusPublished - 2015
Event22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey
Duration: Nov 9 2015Nov 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9491
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other22nd International Conference on Neural Information Processing, ICONIP 2015
CountryTurkey
CityIstanbul
Period11/9/1511/12/15

Fingerprint

Image denoising
Image Denoising
Information Processing
Wavelet transforms
Image quality
Image processing
Sampling
Basis Pursuit
Matching Pursuit
Compressive Sensing
Medical Image
Thresholding
Denoising
Valuation
Image Quality
Wavelet Transform
Image Processing
Tend
Transform
Experimental Results

Keywords

  • ATVD
  • BP
  • CS
  • OMP
  • Sparse

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Rabiul Islam, S. M., Huang, X., Ou, K-L., Rojas, R. F., & Cui, H. (2015). Novel information processing for image de-noising based on sparse basis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9491, pp. 443-451). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9491). Springer Verlag. https://doi.org/10.1007/978-3-319-26555-1_50

Novel information processing for image de-noising based on sparse basis. / Rabiul Islam, Sheikh Md; Huang, Xu; Ou, Keng-Liang; Rojas, Raul Fernandez; Cui, Hongyan.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9491 Springer Verlag, 2015. p. 443-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9491).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rabiul Islam, SM, Huang, X, Ou, K-L, Rojas, RF & Cui, H 2015, Novel information processing for image de-noising based on sparse basis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9491, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9491, Springer Verlag, pp. 443-451, 22nd International Conference on Neural Information Processing, ICONIP 2015, Istanbul, Turkey, 11/9/15. https://doi.org/10.1007/978-3-319-26555-1_50
Rabiul Islam SM, Huang X, Ou K-L, Rojas RF, Cui H. Novel information processing for image de-noising based on sparse basis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9491. Springer Verlag. 2015. p. 443-451. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-26555-1_50
Rabiul Islam, Sheikh Md ; Huang, Xu ; Ou, Keng-Liang ; Rojas, Raul Fernandez ; Cui, Hongyan. / Novel information processing for image de-noising based on sparse basis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9491 Springer Verlag, 2015. pp. 443-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{ba04f67e8c6c4e059e43833b67889668,
title = "Novel information processing for image de-noising based on sparse basis",
abstract = "Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality valuation indexes.",
keywords = "ATVD, BP, CS, OMP, Sparse",
author = "{Rabiul Islam}, {Sheikh Md} and Xu Huang and Keng-Liang Ou and Rojas, {Raul Fernandez} and Hongyan Cui",
year = "2015",
doi = "10.1007/978-3-319-26555-1_50",
language = "English",
isbn = "9783319265544",
volume = "9491",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "443--451",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Novel information processing for image de-noising based on sparse basis

AU - Rabiul Islam, Sheikh Md

AU - Huang, Xu

AU - Ou, Keng-Liang

AU - Rojas, Raul Fernandez

AU - Cui, Hongyan

PY - 2015

Y1 - 2015

N2 - Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality valuation indexes.

AB - Image de-noising is one of the important information processing technologies and a fundamental image processing step for improving the overall quality of medical images. Conventional de-noising methods, however, tend to over-suppress high-frequency details. To overcome this problem, in this paper we present a novel compressive sensing (CS) based noise removing algorithm using proposed sparse basis on CDF9/7 wavelet transform. The measurement matrix is applied to the transform coefficients of the noisy image for compressive sampling. The orthogonal matching pursuit (OMP) and Basis Pursuit (BP) are applied to reconstruct image from noisy sparse image. In the reconstruction process, the proposed threshold with Bayeshrink thresholding strategies is used. Experimental results demonstrate that the proposed method removes noise much better than existing state-of-the-art methods in the sense image quality valuation indexes.

KW - ATVD

KW - BP

KW - CS

KW - OMP

KW - Sparse

UR - http://www.scopus.com/inward/record.url?scp=84951968504&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84951968504&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-26555-1_50

DO - 10.1007/978-3-319-26555-1_50

M3 - Conference contribution

AN - SCOPUS:84951968504

SN - 9783319265544

VL - 9491

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 443

EP - 451

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -