Curvelet Transform Based Image Denoising Via Gaussian Mixture Model

被引：0

作者：

Engin, M. Alptekin ^{[1
]}

Cavusoglu, Bulent ^{[1
]}

机构：

[1] Ataturk Univ, Elekt Elekt Muhendisligi Bolumu, Erzurum, Turkey

来源：

2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2014年

关键词：

Curvelet transform; denoising; Gaussian mixture model;

D O I：

暂无

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：

This paper presents a novel image denoising method based on curvelet transform and gaussian mixture model. After decomposing noisy images into curvelet domain, gaussian mixture model (GMM) is applied and obtained statistical parameters are used for calculating adaptive level depended thresholds. Noise removal is performed using hard threshold method in the curvelet coefficients of each sub-band. Due to the adaptive thresholding for each level the restored images are visually satisfactory.

引用

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页码：1499 / 1502

页数：4

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