一种基于改进阈值函数Contourlet域的图像去噪算法

doi:10.16180/j.cnki.issn1007-7820.2019.04.010

Abstract

Abstract: Aim

ing at the problem of ringing and pseudo-Gibbs caused by the discontinuity of hard threshold function in the Contourlet domain and the problem of image blur distortion caused by constant value compression in the soft threshold function, a Contourlet domain image denoising method based on improved threshold function was proposed.The improved threshold function introduced the idea of exponential smoothing function method to make it continuous, gradual, deviating and high-order conductibility in the Contourlet domain, which overcame the problems of soft and hard threshold functions. In this method, the BayesShrink adaptive threshold estimation was used in the threshold estimation part, which could accurately determine the threshold size and solved the phenomenon that traditional fixed threshold estimation overkill the transform coefficient. Compared with the traditional denoising methods, the improved image denoising method proposed in this paper had better denoising effect in the objective evaluation criteria including peak signal-to-noise ratio, root mean square error and image enhancement factor.

Key words: image denoising, Contourlet domain, improved threshold function, BayesShrink threshold estimation, peak signal-to-noise ratio, root mean square error, image enhancement factor

CLC Number:

TN911.73

WANG Hongchuang,HU Xiaohui,LI Wei. An Image Denoising Algorithm Based on Improved Threshold Function Contourlet Domain[J].Electronic Science and Technology, 2019, 32(4): 44-48.

Figures/Tables 9

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References 19

[1]	冈萨雷斯. 数字图像处理[M]. 北京: 电子工业出版社, 2008.
	Gonzales. Digital image processing [D]. Beijing: Publishing House of Electronics Industry, 2008.
[2]	郭静, 罗华, 张涛 . 机器视觉与应用[J]. 电子科技, 2014,27(7):185-188.
	Guo Jing, Luo Hua, Zhang Tao . Machine vision and application[J]. Electronic Science and Technology, 2014,27(7):185-188.
[3]	焦李成, 谭山 . 图像的多尺度几何分析:回顾和展望[J]. 电子学报, 2003,31(1):1975-1981. doi: 10.3321/j.issn:0372-2112.2003.z1.007
	Jiao Licheng, Tan Shan . Multiscale geometric analysis of images: Retrospect and Prospect[J]. Acta Electronica Sinica, 2003,31(1):1975-1981. doi: 10.3321/j.issn:0372-2112.2003.z1.007
[4]	刘宇, 陈胜 . 医学图像分割方法综述[J]. 电子科技, 2017,30(8):169-172. doi: 10.16180/j.cnki.issn1007-7820.2017.08.047
	Liu Yu, Chen Sheng . A review of medical image segmentation methods[J]. Electronic Science and Technology, 2017,30(8):169-172. doi: 10.16180/j.cnki.issn1007-7820.2017.08.047
[5]	Do M N, Vetterli M. Contourlets: a directional multiresolution image representation [C].Rochester: International Conference on Image Processing, 2002.
[6]	戴维, 于盛林, 孙栓 . 基于Contourlet变换自适应阈值的图像去噪算法[J]. 电子学报, 2007(10):1939-1943. doi: 10.3321/j.issn:0372-2112.2007.10.023
	Dai Wei, Yu Shenglin, Sun Suan . An adaptive threshold algorithm for image denoising based on Contourlet transform[J]. Acta Electronica Sinica, 2007(10):1939-1943. doi: 10.3321/j.issn:0372-2112.2007.10.023
[7]	王瑞, 张友纯 . 新阈值函数下的小波阈值去噪[J]. 计算机工程与应用, 2013,49(15):215-218. doi: 10.3778/j.issn.1002-8331.1211-0364
	Wang Rui, Zhang Youchun . The wavelet threshold de-noising under the new threshold function[J]. Computer Engineering and Applications, 2013,49(15):215-218. doi: 10.3778/j.issn.1002-8331.1211-0364
[8]	Lu J Y, Hong L, Dong Y , et al. A new wavelet threshold function and denoising application[J]. Mathematical Problems in Engineering, 2016(3):1-8.
[9]	Ruikar S D, Doye D D . Wavelet based image denoising technique[J]. International Journal of Advanced Computer Science and Applications, 2011,2(3):49-53.
[10]	唐飞, 杨恢先, 曾友伟 , 等. 改进的Contourlet变换的图像去噪算法[J]. 计算机工程与应用, 2014,50(5):132-136. doi: 10.3778/j.issn.1002-8331.1209-0285
	Tang Fei, Yang Huixian, Zeng Youwei , et al. Improved image denoising algorithm based on Contourlet transform[J]. Computer Engineering and Applications, 2014,50(5):132-136. doi: 10.3778/j.issn.1002-8331.1209-0285
[11]	Li C J, Yang H X, Cai Y Y, et al. Image denoising algorithm based on Non-Subsampled Contourlet transform and bilateral filtering [C]. Bangkok:International Conference on Computer Information Systems and Industrial Applications, 2015.
[12]	Min D, Zhang J, Ma Y . Image denoising via bivariate shrinkage function based on a new structure of dual contourlet transform[J]. Signal Processing, 2015,109(9):25-37. doi: 10.1016/j.sigpro.2014.10.017
[13]	纪建, 许双星, 李晓 . 基于形态成分分析和Contourlet变换的自适应阈值图像去噪方法[J]. 模式识别与人工智能, 2014,27(6):561-568. doi: 10.3969/j.issn.1003-6059.2014.06.012
	Ji Jian, Xu Shuangxing, Li Xiao . Adaptive threshold image denoising method based on morphological component analysis and Contourlet transform[J]. Pattern Recognition and Artificial Intelligence, 2014,27(6):561-568. doi: 10.3969/j.issn.1003-6059.2014.06.012
[14]	郭雷, 刘坤 . 基于非下采样Contourlet变换的自适应图像融合算法[J]. 西北工业大学学报, 2009,27(2):255-259. doi: 10.3969/j.issn.1000-2758.2009.02.022
	Guo Lei, Liu Kun . Adaptive image fusion algorithm based on non down sampling Contourlet transform[J]. Journal of Northwestern Polytechnical University, 2009,27(2):255-259. doi: 10.3969/j.issn.1000-2758.2009.02.022
[15]	Po D D, Do M N . Directional multiscale modeling of images using the contourlet transform[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2006,15(6):1610-1620. doi: 10.1109/TIP.2006.873450 pmid: 16764285
[16]	Shahdoosti H R, Hazavei S M . Image denoising in dual contourlet domain using hidden Markov tree models[J]. Digital Signal Processing, 2017,67(3):17-29. doi: 10.1016/j.dsp.2017.04.011
[17]	Hashemi M, Beheshti S . Adaptive noise variance estimation in BayesShrink[J]. IEEE Signal Processing Letters, 2010,17(1):12-15. doi: 10.1109/LSP.2009.2030856
[18]	周风波, 李长庚, 朱红求 . 基于提升小波变换的阈值改进去噪算法在紫外可见光谱中的研究[J]. 光谱学与光谱分析, 2018,38(2):506-510.
	Zhou Fengbo, Li Changgeng, Zhu Hongqiu . Improved threshold denoising algorithm based on lifting wavelet transform and its application in UV Vis spectroscopy[J]. Spectroscopy and Spectral Analysis, 2018,38(2):506-510.
[19]	张维强, 宋国乡 . 基于一种新的阈值函数的小波域信号去噪[J]. 西安电子科技大学学报, 2004(2):296-299. doi: 10.3969/j.issn.1001-2400.2004.02.030
	Zhang Weiqiang, Song Guoxiang . Signal denoising in wavelet domain based on a new threshold function[J]. Journal of Xidian University, 2004(2):296-299. doi: 10.3969/j.issn.1001-2400.2004.02.030

方法	PSNR	MSE	IEF
噪声图像	20.301 7	606.615 7	—
小波域硬阈值函数	26.877 4	133.456 9	4.548 6
小波域软阈值函数	25.997 4	163.433 4	3.710 7
小波域改进阈值函数	27.718 0	109.970 4	5.516 2
Contourlet硬阈值函数	26.638 0	141.018 5	4.304 7
Contourlet软阈值函数	26.304 6	152.271 0	3.982 7
本文算法	27.901 0	105.422 7	5.753 5

方法	PSNR	MSE	IEF
噪声图像	20.404 8	573.571 0	-
小波域硬阈值函数	26.970 1	130.639 6	4.405 9
小波域软阈值函数	26.630 4	141.267 3	4.091 2
小波域改进阈值函数	28.303 7	96.097 2	5.968 7
Contourlet硬阈值函数	26.859 5	134.006 7	4.295 2
Contourlet软阈值函数	26.893 9	132.949 6	4.347 1
本文算法	28.414 0	93.688 3	6.122 1

An Image Denoising Algorithm Based on Improved Threshold Function Contourlet Domain

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