@inproceedings{Shadaydeh17_IDCSAR,
    type = {inproceedings},
    key = {Shadaydeh17_IDCSAR},
    title = {Image denoising via collaborative support-agnostic recovery},
    author = {Muzammil Behzad and Mudassir Masood and Tarig Ballal and Maha Shadaydeh and Tareq. Y. Al-Naffouri},
    booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    year = {2017},
    month = {March},
    pages = {1343-1347},
    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.},
    doi = {10.1109/ICASSP.2017.7952375},
    url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7952375&isnumber=7951776},
}