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


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摘要We 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.
著者Li JZ, Luisier F, Blu T
會議名稱IEEE International Conference on Image Processing (ICIP'16)
會議開始日25.09.2016
會議完結日28.09.2016
會議地點Phoenix, AZ
會議國家/地區美國
期刊名稱2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
會議論文集題名Image Processing (ICIP), 2016 IEEE International Conference on
出版作品名稱2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
出版年份2016
出版社IEEE
頁次2708 - 2712
國際標準書號978-1-4673-9962-3
電子國際標準書號978-1-4673-9961-6
國際標準期刊號1522-4880
語言英式英語
關鍵詞Image deconvolution,Poisson noise,unbiased risk estimate,MSE estimation
Web of Science 學科類別Engineering, Electrical & Electronic;Imaging Science & Photographic Technology;Engineering;Imaging Science & Photographic Technology

上次更新時間 2021-19-02 於 01:01