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Proceedings Paper

Image deconvolution under Poisson noise using SURE-LET approach
Author(s): Feng Xue; Jiaqi Liu; Gang Meng; Jing Yan; Min Zhao
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Paper Abstract

We propose an image deconvolution algorithm when the data is contaminated by Poisson noise. By minimizing Stein's unbiased risk estimate (SURE), the SURE-LET method was firstly proposed to deal with Gaussian noise corruption. Our key contribution is to demonstrate that the SURE-LET algorithm is also applicable for Poisson noisy image and proposed an efficient algorithm.

The formulation of SURE requires knowledge of Gaussian noise variance. We experimentally found a simple and direct link between the noise variance estimated by median absolute difference (MAD) method and the optimal one that leads to the best deconvolution performance in terms of mean squared error (MSE). Extensive experiments show that this optimal noise variance works satisfactorily for a wide range of natural images.

Paper Details

Date Published: 8 October 2015
PDF: 7 pages
Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750B (8 October 2015); doi: 10.1117/12.2197291
Show Author Affiliations
Feng Xue, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Jiaqi Liu, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Gang Meng, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Jing Yan, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)
Min Zhao, National Key Lab. of Science and Technology on Test Physics and Numerical Mathematics (China)


Published in SPIE Proceedings Vol. 9675:
AOPC 2015: Image Processing and Analysis
Chunhua Shen; Weiping Yang; Honghai Liu, Editor(s)

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