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    Image denoising via collaborative support-agnostic recovery

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    1609.02932.pdf
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    Type
    Conference Paper
    Authors
    Behzad, Muzammil
    Masood, Mudassir cc
    Ballal, Tarig
    Shadaydeh, Maha
    Al-Naffouri, Tareq Y. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    KAUST Grant Number
    OSR 2016-KKI-2899
    Date
    2017-06-20
    Online Publication Date
    2017-06-20
    Print Publication Date
    2017-03
    Permanent link to this record
    http://hdl.handle.net/10754/625625
    
    Metadata
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    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.
    Citation
    Behzad M, Masood M, Ballal T, Shadaydeh M, Al-Naffouri TY (2017) Image denoising via collaborative support-agnostic recovery. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/ICASSP.2017.7952375.
    Sponsors
    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.
    Publisher
    IEEE
    Journal
    2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
    Conference/Event name
    2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
    DOI
    10.1109/ICASSP.2017.7952375
    arXiv
    1609.02932
    Additional Links
    http://ieeexplore.ieee.org/document/7952375/
    http://arxiv.org/pdf/1609.02932
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICASSP.2017.7952375
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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