KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberOSR 2016-KKI-2899
Online Publication Date2017-06-20
Print Publication Date2017-03
Permanent link to this recordhttp://hdl.handle.net/10754/625625
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AbstractIn 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.
CitationBehzad 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.
SponsorsThis 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.
Conference/Event name2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017