Image de-noising model of combining Curvelet and LLT model

doi:10.3778/j.issn.1002-8331.2010.25.049

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (25): 168-170.DOI: 10.3778/j.issn.1002-8331.2010.25.049

• 图形、图像、模式识别 • Previous Articles Next Articles

Image de-noising model of combining Curvelet and LLT model

WANG Ling-hua，YANG Jia-hong

College of Polytechnic，Hunan Normal University，Changsha 410081，China

Received:2009-10-30 Revised:2009-12-16 Online:2010-09-01 Published:2010-09-01
Contact: WANG Ling-hua

结合Curvelet和LLT模型的图像去噪模型

王玲华，杨家红

湖南师范大学，长沙 410081

通讯作者: 王玲华

Abstract

Abstract: Curvelet is able to remove noise thoroughly，but the staircase effect is easily brought about.Smooth regions are protected well and the staircase is not produced by four-order partial differential equation LLT model.A new model based on Curvelet and fourth-order PDE is proposed by combining Curvelet and LLT model with weight function λ.The experiment results have demonstrated that the new model can preserve the advantages of Curvelet and LLT，at the same time，the de-noising rate and visual effect are superior to both of them.

Key words: image de-noising, weight function λ, Curvelet, fourth-order partial differential equation, LLT model

摘要： Curvelet去噪彻底，易产生“阶梯”效应，四阶偏微分方程LLT模型去噪能很好地保护图像细节特征，不会产生阶梯效应。提出了一个模型将Curvelet去噪和四阶偏微分方程中LLT模型去噪的优点通过权函数λ结合起来，实验表明，新的模型能很好地发挥二者的优点，不但很好地保护图像细节特征，消除阶梯效应，去噪彻底且能很好地保持边缘，去噪率和视觉效果优于Curvelet和LLT方法。

关键词: 图像去噪, 权函数λ, Curvelet, 四阶偏微分方程, LLT模型

CLC Number:

TP391

WANG Ling-hua，YANG Jia-hong. Image de-noising model of combining Curvelet and LLT model[J]. Computer Engineering and Applications, 2010, 46(25): 168-170.

王玲华，杨家红. 结合Curvelet和LLT模型的图像去噪模型[J]. 计算机工程与应用, 2010, 46(25): 168-170.

[1]	YANG Li1, WU Guifang1, SUN Xiuming2. Algorithm of pavement crack fusion based on multi-scale curvelet transforms [J]. Computer Engineering and Applications, 2016, 52(6): 182-185.
[2]	LI Xinchun, YU Shuping, WANG Bo. Improved non-local means algorithm [J]. Computer Engineering and Applications, 2016, 52(5): 185-189.
[3]	HU Yu1, XIE Kai1, RUAN Ningjun1, WU Peng2，3, HE Jianbiao4, LIU Conghao1. Method of weak signal recovery based on 3D-curvelet transform [J]. Computer Engineering and Applications, 2016, 52(18): 199-202.
[4]	CAI Weihua, HE Xuansen. Noisy blind source separation based on undecimated wavelet transform and independent component analysis [J]. Computer Engineering and Applications, 2016, 52(16): 180-185.
[5]	SONG Xinchao, SU Qingtang, LIU Qiming. Multi-pose ear recognition based on improved locally linear embedding algorithm [J]. Computer Engineering and Applications, 2016, 52(13): 183-186.
[6]	YI Qingming, CHEN Mingmin, SHI Min. Improved wavelet de-noising method for infrared image application [J]. Computer Engineering and Applications, 2016, 52(1): 173-177.
[7]	WANG Jue1，2, LUO Shan1, ZOU Yongning2. Non-local means of CT image de-noising method based on neighborhood window [J]. Computer Engineering and Applications, 2015, 51(9): 168-172.
[8]	XU Jie1，2，3, CUI Yaoyao1, XIANG Yongjia1, LV Tiejun1, GU Tianming1. Curvelet denoising method of ultrasound images based on probability distribution [J]. Computer Engineering and Applications, 2015, 51(19): 189-193.
[9]	HUANG Yuchang, HOU Dewen. Fingerprint de-noising based on improved wavelet threshold function [J]. Computer Engineering and Applications, 2014, 50(6): 179-181.
[10]	SONG Shulin, ZHANG Yan, WANG Xian, MAO Qibo. Illumination processing method based on Curvelet transform and Retinex [J]. Computer Engineering and Applications, 2013, 49(3): 171-173.
[11]	YANG Xingming1, CHEN Haiyan1, WANG Gang1, WANG Binbin1, ZHAO Yinping2. Improvement of HMT based on uniform discrete curvelet coefficients and application in image denoising [J]. Computer Engineering and Applications, 2013, 49(18): 195-199.
[12]	LIN Kezheng, WANG Hao. Edge texture feature extraction and expression recognition based on Curvelet [J]. Computer Engineering and Applications, 2013, 49(16): 151-154.
[13]	YANG Hongjuan1, CHEN Jiwen2, ZHANG Yunchu1. Surface feature extraction based on curvelet transform from point cloud [J]. Computer Engineering and Applications, 2012, 48(33): 183-187.
[14]	WU Fei1，2, ZHANG Dexiang1，2. Research on fusion of multi-band remote sensing image based on Curvelet transforms [J]. Computer Engineering and Applications, 2012, 48(32): 153-156.
[15]	CHEN Yinyin, HUANG Youdu. Image de-noising algorithm based on spatial correlation filtering and TV model [J]. Computer Engineering and Applications, 2012, 48(31): 175-178.

Image de-noising model of combining Curvelet and LLT model

结合Curvelet和LLT模型的图像去噪模型

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics