Image denoising with Gaussian mixture model

被引：11

作者：

Cao, Yang ^{[1
]}

Luo, Yupin ^{[1
]}

Yang, Shiyuan ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

CISP 2008: FIRST INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOL 3, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/CISP.2008.312

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we present a Gaussian mixture model (GMM) based method for image denoising. The method partitions an image into a set of overlapping patches, and assumes that the image patches are random,, variables described by a GMM. The distribution parameters of the noise free image patches are estimated from the noisy parameters which are calculated by expectation maximization (EM). Minimum mean square error (MMSE) estimation technique is used to estimate the clean image patches. The experimental results show that new method can effectively suppress additive noise and preserve details of image signal.

引用

页码：339 / 343

页数：5

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