Deconvolution of poissonian images with the PURE-LET approach
Journal
Proceedings - International Conference on Image Processing, ICIP
Journal Volume
2016-August
ISBN
9781467399616
Date Issued
2016-08-03
Author(s)
Abstract
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.
Subjects
Image deconvolution | MSE estimation | Poisson noise | Unbiased risk estimate
Type
conference paper