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Image Denoising with Non-Local Means in the Shearlet Domain

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3 Author(s)
Xiaohua Zhang ; Key Lab. of Intell. Perception & Image Understanding of the Minist. of Educ. of China, Xidian Univ., Xi''an, China ; Qiang Zhang ; Jiao, L.C.

In this paper, a novel method for image denoising is proposed which adopts multiscale geometry tool. Firstly the image is decomposed by discrete shearlet transform. The shearlet coefficients of each direction approach the generalized Gaussian distribution. We use the principal component analysis (PCA) for every similarity window of shearlet coefficients. Then we use Generalized Gaussian model of non-local means method to handle the shearlet coefficients. Finally, we reconstruct image with the new shearlet coefficients to obtain the result. Numerical results show that our algorithm competes favorably with nonlocal means algorithms in the case of high noise.

Published in:

Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), 2011 International Workshop on

Date of Conference:

10-12 Jan. 2011

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