Minimum risk wavelet shrinkage operator for Poisson image denoising

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Wu Cheng, Keigo Hirakawa

Abstract

The pixel values of images taken by an image sensor are said to be corrupted by Poisson noise. To date, multiscale Poisson image denoising techniques have processed Haar frame and wavelet coefficients--the modeling of coefficients is enabled by the Skellam distribution analysis. We extend these results by solving for shrinkage operators for Skellam that minimizes the risk functional in the multiscale Poisson image denoising setting. The minimum risk shrinkage operator of this kind effectively produces denoised wavelet coefficients with minimum attainable L2 error.

References

Sep 21, 2004·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhou WangEero P Simoncelli
Feb 26, 2005·Perception·Andriana Olmos, Frederick A A Kingdom
Aug 11, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Kostadin DabovKaren Egiazarian
Feb 5, 2008·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Javier PortillaEero P Simoncelli
Sep 14, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Mário A T Figueiredo, José M Bioucas-Dias
Sep 16, 2010·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Florian LuisierMichael Unser
Jun 14, 2012·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Markku Mäkitalo, Alessandro Foi

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Citations

Nov 2, 2016·Journal of X-ray Science and Technology·Thomas W RogersLewis D Griffin
Jan 17, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jiachao Zhang, Keigo Hirakawa

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