Full blind denoising through noise covariance estimation using Gaussian scale mixtures in the wavelet domain

被引：0

作者：

Portilla, J ^{[1
]}

机构：

[1] Univ Granada, Visual Informat Proc Grp, Dept Comp Sci & Artificial Intelligence, Granada, Spain

来源：

ICIP: 2004 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1- 5 | 2004年

关键词：

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

We describe an efficient generalized expectation maximization algorithm for estimating the spectral features of a noise source corrupting an observed image. We use a statistical model for images decomposed in on overcomplete oriented pyramid. Each neighborhood of clean pyramid coefficients is modelled as a Gaussian scale mixture, whereas the noise is assumed Gaussion. Combining this GEM technique with a previous Bayesian denoise estimator, we obtain a full blind denoising algorithm, able to deal with homogeneous, Gaussian or mesokurtotic, noise sources of arbitrary covoriance. Results demonstrate the high performance of the method for a wide range of corruption sources.

引用

页码：1217 / 1220

页数：4

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