Image deconvolution under Poisson noise using SURE-LET approach

Feng Xue; Jiaqi Liu; Gang Meng; Jing Yan; Min Zhao

doi:10.1117/12.2197291

8 October 2015 Image deconvolution under Poisson noise using SURE-LET approach

Feng Xue, Jiaqi Liu, Gang Meng, Jing Yan, Min Zhao

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.

Citation Download Citation

Feng Xue, Jiaqi Liu, Gang Meng, Jing Yan, and Min Zhao "Image deconvolution under Poisson noise using SURE-LET approach", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750B (8 October 2015); https://doi.org/10.1117/12.2197291

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