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

摘要/Abstract

摘要：

针对Contourlet域中传统硬阈值函数由于函数不连续所造成的振铃和伪吉布斯现象以及软阈值函数由于恒值压缩导致的图像模糊失真的问题,文中提出一种基于改进阈值函数的Contourlet域图像去噪算法。该改进阈值函数引入了指数平滑函数法的思想,使其在Contourlet域内具备连续性、渐进性、偏差性和高阶可导性,克服了软硬阈值函数存在的问题。方法中阈值估计部分选取的是BayesShrink自适应阈值估计,能够比较精准的确定阈值大小,并且解决了传统固定阈值估计过度扼杀变换系数的现象。通过对比实验,文中提出的改进后图像去噪方法在峰值信噪比、均方根误差和图像增强因子等客观评价标准上与传统去噪方法相比具备较好的去噪效果。

关键词: 图像去噪, Contourlet域, 阈值函数, BayesShrink阈值估计, 峰值信噪比, 均方根误差, 图像增强因子

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

中图分类号:

TN911.73

王鸿闯,胡晓辉,李薇. 一种基于改进阈值函数Contourlet域的图像去噪算法[J]. 电子科技, 2019, 32(4): 44-48.

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.

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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