Li, JizhouJizhouLiLuisier, FlorianFlorianLuisierTHIERRY BLU2024-03-072024-03-072016-08-03978146739961615224880https://scholars.lib.ntu.edu.tw/handle/123456789/640497We 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.Image deconvolution | MSE estimation | Poisson noise | Unbiased risk estimateconference paper10.1109/ICIP.2016.75328512-s2.0-85006791259https://api.elsevier.com/content/abstract/scopus_id/85006791259