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 proceedingConference contribution

2 Scopus citations


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 languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)9781509041176
StatePublished - 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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