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

^*Corresponding author for this work

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

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
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1343-1347
Number of pages	5
ISBN (Electronic)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7952375
State	Published - 16 Jun 2017

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

PSNR
SSIM
collaborative estimation
image denoising
similar patches
sparse reconstruction

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2017.7952375

Cite this

Behzad, M., Masood, M., Ballal, T., Shadaydeh, M., & Al-Naffouri, T. Y. (2017). Image denoising via collaborative support-agnostic recovery. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 1343-1347). Article 7952375 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952375

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

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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.",

keywords = "PSNR, SSIM, collaborative estimation, image denoising, similar patches, sparse reconstruction",

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

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language = "English",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

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Behzad, M , Masood, M , Ballal, T, Shadaydeh, M & Al-Naffouri, TY 2017, Image denoising via collaborative support-agnostic recovery. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7952375, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1343-1347. https://doi.org/10.1109/ICASSP.2017.7952375

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 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1343-1347 7952375 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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

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AU - Behzad, Muzammil

AU - Masood, Mudassir

AU - Ballal, Tarig

AU - Shadaydeh, Maha

AU - Al-Naffouri, Tareq Y.

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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.

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KW - similar patches

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Behzad M , Masood M , Ballal T, Shadaydeh M, Al-Naffouri TY. Image denoising via collaborative support-agnostic recovery. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1343-1347. 7952375. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2017.7952375

Image denoising via collaborative support-agnostic recovery

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

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