SPATIALLY ADAPTIVE THRESHOLD FOR IMAGE DENOISNG BASED ON NONSUBSAMPLED CONTOURLET TRANSFORM

被引：0
|
作者：
Sun, Xiangda [1 ]
Nu, Junping [1 ]
Zhou, Yipeng [2 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China
[2] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 102401, Peoples R China
来源：
PROCEEDINGS OF THE 3RD IEEE INTERNATIONAL CONFERENCE ON NETWORK INFRASTRUCTURE AND DIGITAL CONTENT (IEEE IC-NIDC 2012) | 2012年
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Context-modeling; NSCT; Spatially adaptive; Coefficient characteristics; PSNR;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In recent years, the threshold for removing noise based on wavelet transform has been very widely used because of its effectiveness and simplicity. Thus, there has been threshold based on a variety of frequency-domain transform. During the process of denoising, due to the differentiation of transform coefficients generated by noise and edge information, a good threshold for denoising can make a significant impact on the image quality. In currently existing threshold, spatially adaptive threshold based on Context-Modeling is proposed because of having considered neighboring coefficients so that it can adjust to coefficient characteristics. In this paper the improved spatially adaptive threshold method is applied to the nonsubsampled contourlet transform. Experimental results show that the method yields superior image quality and higher PSNR.
引用
收藏
页码：477 / 481
页数：5
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