Novel information processing for image denoising based on sparse basis

Sheikh Md. Rabiul Islam, Xu HUANG, Keng-Liang Ou, Raul FERNANDEZ ROJAS, Hongyan Cui

Research output: A Conference proceeding or a Chapter in BookConference 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 evaluation indexes.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2015)
Subtitle of host publicationLecture notes in computer science
EditorsTingwen Huang, Qingshan Liu, Weng Kin Lai, Sabri Arik
Place of PublicationSwitzerland
PublisherSpringer
Pages443-451
Number of pages9
Volume9491
ISBN (Electronic)9783319265551
ISBN (Print)9783319265544
DOIs
Publication statusPublished - 2015
Event22nd International Conference on Neural Information Processing ICONIP 2015 - Istanbul, Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9491
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Neural Information Processing ICONIP 2015
Abbreviated titleICONIP 2015
CountryTurkey
CityIstanbul
Period9/11/1512/11/15

Fingerprint

Image denoising
Wavelet transforms
Image quality
Image processing
Sampling

Cite this

Islam, S. M. R., HUANG, X., Ou, K-L., FERNANDEZ ROJAS, R., & Cui, H. (2015). Novel information processing for image denoising based on sparse basis. In T. Huang, Q. Liu, W. K. Lai, & S. Arik (Eds.), International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science (Vol. 9491, pp. 443-451). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9491). Switzerland: Springer. https://doi.org/10.1007/978-3-319-26555-1_50
Islam, Sheikh Md. Rabiul ; HUANG, Xu ; Ou, Keng-Liang ; FERNANDEZ ROJAS, Raul ; Cui, Hongyan. / Novel information processing for image denoising based on sparse basis. International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. editor / Tingwen Huang ; Qingshan Liu ; Weng Kin Lai ; Sabri Arik. Vol. 9491 Switzerland : Springer, 2015. pp. 443-451 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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 evaluation indexes.",
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Islam, SMR, HUANG, X, Ou, K-L, FERNANDEZ ROJAS, R & Cui, H 2015, Novel information processing for image denoising based on sparse basis. in T Huang, Q Liu, WK Lai & S Arik (eds), International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. vol. 9491, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9491, Springer, Switzerland, pp. 443-451, 22nd International Conference on Neural Information Processing ICONIP 2015, Istanbul, Turkey, 9/11/15. https://doi.org/10.1007/978-3-319-26555-1_50

Novel information processing for image denoising based on sparse basis. / Islam, Sheikh Md. Rabiul; HUANG, Xu; Ou, Keng-Liang; FERNANDEZ ROJAS, Raul; Cui, Hongyan.

International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. ed. / Tingwen Huang; Qingshan Liu; Weng Kin Lai; Sabri Arik. Vol. 9491 Switzerland : Springer, 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: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Novel information processing for image denoising based on sparse basis

AU - Islam, Sheikh Md. Rabiul

AU - HUANG, Xu

AU - Ou, Keng-Liang

AU - FERNANDEZ ROJAS, Raul

AU - Cui, Hongyan

PY - 2015

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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 evaluation 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 evaluation indexes.

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BT - International Conference on Neural Information Processing (ICONIP 2015)

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PB - Springer

CY - Switzerland

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Islam SMR, HUANG X, Ou K-L, FERNANDEZ ROJAS R, Cui H. Novel information processing for image denoising based on sparse basis. In Huang T, Liu Q, Lai WK, Arik S, editors, International Conference on Neural Information Processing (ICONIP 2015): Lecture notes in computer science. Vol. 9491. Switzerland: Springer. 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