Deconvolution of poissonian images with the pure-let approach
Refereed conference paper presented and published in conference proceedings


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AbstractWe propose a non-iterative image deconvolution algorithm for data corrupted by Poisson noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parametrize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds (LET). This parametrization is then optimized by minimizing a robust estimate of the mean squared error, the "Poisson unbiased risk estimate (PURE)". Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations which has a fast and exact solution. Simulation experiments over various noise levels indicate that the proposed method outperforms current state-of-the-art techniques, in terms of both restoration quality and computational time.
All Author(s) ListLi JZ, Luisier F, Blu T
Name of ConferenceIEEE International Conference on Image Processing (ICIP'16)
Start Date of Conference25/09/2016
End Date of Conference28/09/2016
Place of ConferencePhoenix, AZ
Country/Region of ConferenceUnited States of America
Journal name2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Proceedings TitleImage Processing (ICIP), 2016 IEEE International Conference on
Title of Publication2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Year2016
PublisherIEEE
Pages2708 - 2712
ISBN978-1-4673-9962-3
eISBN978-1-4673-9961-6
ISSN1522-4880
LanguagesEnglish-United Kingdom
KeywordsImage deconvolution,Poisson noise,unbiased risk estimate,MSE estimation
Web of Science Subject CategoriesEngineering, Electrical & Electronic;Imaging Science & Photographic Technology;Engineering;Imaging Science & Photographic Technology

Last updated on 2020-18-10 at 02:20