Feature-Preserving Noise Removal

IEEE Transactions on Medical Imaging
Khalid YoussefLouis-S Bouchard

Abstract

Conventional image restoration algorithms use transform-domain filters, which separate the noise from the sparse signal among the transform components or apply spatial smoothing filters in real space whose design relies on prior assumptions about the noise statistics. These filters also reduce the information content of the image by suppressing spatial frequencies or by recognizing only a limited set of shapes. Here we show that denoising can be efficiently done using a nonlinear filter, which operates along patch neighborhoods and multiple copies of the original image. The use of patches enables the algorithm to account for spatial correlations in the random field whereas the multiple copies are used to recognize the noise statistics. The nonlinear filter, which is implemented by a hierarchical multistage system of multilayer perceptrons, outperforms state-of-the-art denoising algorithms such as those based on collaborative filtering and total variation. Compared to conventional denoising algorithms, our filter can restore images without blurring them, making it attractive for use in medical imaging where the preservation of anatomical details is critical.

References

Sep 21, 2004·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhou WangEero P Simoncelli
Dec 13, 2006·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Michael Elad, Michal Aharon
Aug 11, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Kostadin DabovKaren Egiazarian
Jan 1, 1994·IEEE Transactions on Neural Networks·M T Hagan, M B Menhaj
Apr 9, 2008·IEEE Transactions on Medical Imaging·P CoupeC Barillot
Dec 23, 2009·Journal of Magnetic Resonance Imaging : JMRI·José V ManjónMontserrat Robles
Apr 27, 2010·Medical Image Analysis·Pierrick CoupéD Louis Collins
Feb 5, 2011·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Lin ZhangDavid Zhang
May 17, 2011·Medical Image Analysis·José V ManjónMontserrat Robles
Aug 21, 2013·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Ruomei YanYan Liu
Aug 21, 2013·Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft·N ArendK H Eibl-Lindner
Aug 24, 2013·International Journal of Oncology·Daniele MaggioniGabriella Nicolini

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