8 October 2015 Image deconvolution under Poisson noise using SURE-LET approach
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96750B (2015) https://doi.org/10.1117/12.2197291
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Feng Xue, Feng Xue, Jiaqi Liu, Jiaqi Liu, Gang Meng, Gang Meng, Jing Yan, Jing Yan, Min Zhao, Min Zhao, } "Image deconvolution under Poisson noise using SURE-LET approach", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750B (8 October 2015); doi: 10.1117/12.2197291; https://doi.org/10.1117/12.2197291
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