Image denoising via collaborative support-agnostic recovery

Muzammil Behzad; Mudassir Masood; Tarig Ballal; Maha Shadaydeh; Tareq Y. Al-Naffouri

doi:10.1109/ICASSP.2017.7952375

Image denoising via collaborative support-agnostic recovery

Muzammil Behzad, Mudassir Masood, Tarig Ballal, Maha Shadaydeh, Tareq Y. Al-Naffouri

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

2 Scopus citations

Abstract

In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.

Original language	English (US)
Title of host publication	2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1343-1347
Number of pages	5
ISBN (Print)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7952375
State	Published - Jun 20 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR 2016-KKI-2899
Acknowledgements: This work is supported in part by the KAUST Office of Sponsored Research under Award No. OSR 2016-KKI-2899, and by Deanship of Scientific Research at KFUPM, Saudi Arabia, through project number KAUST-002.

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10.1109/ICASSP.2017.7952375

Cite this

@inproceedings{59583986f1f84335a7cbc1ad5610ff7b,

title = "Image denoising via collaborative support-agnostic recovery",

abstract = "In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.",

author = "Muzammil Behzad and Mudassir Masood and Tarig Ballal and Maha Shadaydeh and Al-Naffouri, {Tareq Y.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR 2016-KKI-2899 Acknowledgements: This work is supported in part by the KAUST Office of Sponsored Research under Award No. OSR 2016-KKI-2899, and by Deanship of Scientific Research at KFUPM, Saudi Arabia, through project number KAUST-002.",

year = "2017",

month = jun,

day = "20",

doi = "10.1109/ICASSP.2017.7952375",

language = "English (US)",

isbn = "9781509041176",

pages = "1343--1347",

booktitle = "2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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Image denoising via collaborative support-agnostic recovery. / Behzad, Muzammil; Masood, Mudassir; Ballal, Tarig et al.
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 1343-1347.

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

TY - GEN

T1 - Image denoising via collaborative support-agnostic recovery

AU - Behzad, Muzammil

AU - Masood, Mudassir

AU - Ballal, Tarig

AU - Shadaydeh, Maha

AU - Al-Naffouri, Tareq Y.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR 2016-KKI-2899 Acknowledgements: This work is supported in part by the KAUST Office of Sponsored Research under Award No. OSR 2016-KKI-2899, and by Deanship of Scientific Research at KFUPM, Saudi Arabia, through project number KAUST-002.

PY - 2017/6/20

Y1 - 2017/6/20

N2 - In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.

AB - In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.

UR - http://hdl.handle.net/10754/625625

UR - http://ieeexplore.ieee.org/document/7952375/

UR - http://www.scopus.com/inward/record.url?scp=85023752031&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2017.7952375

DO - 10.1109/ICASSP.2017.7952375

M3 - Conference contribution

SN - 9781509041176

SP - 1343

EP - 1347

BT - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Image denoising via collaborative support-agnostic recovery

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