用噪声检测算法改进理想低通滤波器

doi:10.11772/j.issn.1001-9081.2014.10.2971

计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 2971-2975.DOI: 10.11772/j.issn.1001-9081.2014.10.2971

用噪声检测算法改进理想低通滤波器

杨柱中¹,²,³,周激流²,⁴,郎方年²

1. 成都大学电子信息工程学院，成都 610106；
2. 成都大学模式识别与智能信息处理实验室，成都 610106；
3. 深圳市高性能数据挖掘重点实验室（中国科学院），广东深圳 518055；
4. 四川大学计算机（软件）学院，成都 610064

收稿日期:2014-02-26 修回日期:2014-04-16 出版日期:2014-10-01 发布日期:2014-10-30
通讯作者: 杨柱中
作者简介:杨柱中（1968-），男，湖南湘乡人，副教授，博士，主要研究方向：数字信号处理、计算智能；
周激流（1963-），男，四川威远人，教授，博士生导师，主要研究方向：计算机图形图像处理、计算智能、信息隐藏、分数阶微积分算法、信息处理；
郎方年（1974-），男，江苏苏州人，副教授，博士，主要研究方向：数字图像处理、生物特征识别、人工智能。
基金资助:
深圳市生物、互联网、新能源产业发展专项资金资助项目

Improving ideal low-pass filter with noise detection algorithm

YANG Zhuzhong¹,²,³,ZHOU Jiliu²,⁴,LANG Fangnian²

1. College of Electronics and Information Engineering, Chengdu University, Chengdu Sichuan 610106, China;
2. Laboratory of Pattern Recognition and Intelligent Information Processing, Chengdu University, Chengdu Sichuan 610106, China;
3. Shenzhen Key Laboratory of High Performance Data Mining (Chinese Academy of Sciences), Shenzhen Guangdong 518055, China;
4. College of Computer Science (Software), Sichuan University, Chengdu Sichuan 610064, China

Received:2014-02-26 Revised:2014-04-16 Online:2014-10-01 Published:2014-10-30
Contact: YANG Zhuzhong

摘要/Abstract

摘要：

针对图像去噪算法存在滤除噪声与保留图像边缘细节之间的矛盾，提出了一种使用基于分数阶微分梯度的随机噪声检测算法来提高理想低通滤波器的去噪性能的方法。首先，使用不同方向的分数阶微分梯度模板与含噪声图像进行卷积，计算出图像在不同方向上的分数阶微分梯度；然后，依据预先设定的阈值获得不同方向的分数阶微分梯度检测图，将在所有选定方向上梯度都发生跳变的像素点判定为噪声点；最后，只对图像中被检测出的噪声点用理想低通滤波器进行滤波，可使图像在去除噪声和保留图像细节两方面同时获得较优的效果。实验结果表明，所提算法不仅可以获得更好的视觉效果，而且去噪后图像的峰值性噪比(PSNR)表明去噪后的图像更接近原始图像，使用理想低通滤波器获得的最大PSNR为29.0893dB，所提算法获得的最PSNR为34.7027dB。将分数阶微积分用于图像去噪，为提高图像去噪性能提供了一个新的研究方向。

Abstract:

For the contradiction between filtering noise and preserving image detail in image denoising algorithms, a random noise detection algorithm based on fractional differential gradient, was proposed to improve denoising performance of the ideal low-pass filter in this paper. Firstly, the fractional differential gradient templates of different directions were used to convolve with noisy images, and calculate fractional differential gradients in different directions. Then according to a pre-set threshold value, the fractional differential gradient detection figures in different directions could be obtained. If the pixel gradients occurred hopping in all selected directions, and this pixel was determined to be a noise pixel. Finally, only the detected noise pixels were processed by ideal low-pass filter. The denoised image could get a better effect of removing the noise and preserving image detail at the same time. The experimental results show that the proposed algorithm can get a better visual effect, the Peak Signal-to-Noise Ratio (PSNR) of denoised image indicates the denoised image is more closer to the original image: The maximum PSNR by using the ideal low-pass filter is 29.0893dB, meanwhile the maximum PSNR obtained by the proposed algorithm is 34.7027dB. It is an exploration of fractional calculus for image denoising, and provides a new research direction to improve performance of image denoising.

中图分类号:

TP391.41

杨柱中周激流郎方年. 用噪声检测算法改进理想低通滤波器[J]. 计算机应用, 2014, 34(10): 2971-2975.

YANG Zhuzhong ZHOU Jiliu LANG Fangnian. Improving ideal low-pass filter with noise detection algorithm[J]. Journal of Computer Applications, 2014, 34(10): 2971-2975.

参考文献

［1］BUADES A, COLL B, MOREL J M. A review of image denoising algorithms, with a new one ［J］. SIAM Journal on Multiscale Modeling and Simulation, 2005,4(2):490-530.
［2］LIU Z, ZOU D, DENG C, et al.A new de-noising algorithm for removal impulse noise based on detection of noise points ［J］.Computer Engineering and Applications, 2005(15):41-43. (刘祝华,邹道文,邓承志,等.一种新的基于噪声点检测的脉冲噪声去噪算法［J］.计算机工程与应用,2005(15):41-43.)［3］GAO K, GUO J. An adaptive median filtering algorithm based on noise detection ［J］. Journal of Fujian Agriculture and Forestry University: Natural Science Edition, 2009,38(3):333-336.(高克芳,郭建钢.一种基于噪声点检测的自适应中值滤波方法［J］.福建农林大学学报:自然科学版,2009,38(3):333-336.)
［4］OLDHAM K B, SPANIER J. The fractional calculus ［M］. New York: Academic Press, 1974.
［5］PODLUBNY I. Fractional differential equations ［M］. New York: Academic Press, 1999.
［6］LOVERRO A. Fractional calculus: history, definitions and applications for the engineer ［EB/OL］. ［2013-12-02］. http://www.docin.com/p-387741200.html.
［7］PU Y, WANG W. Fractional differential masks of digital image and their numerical implementation algorithms ［J］. Acta Automatica Sinica, 2007,33(11):1128-1135.(蒲亦非,王卫星.数字图像的分数阶微分掩模及其数值运算规则［J］.自动化学报,2007,33(11):1128-1135.)
［8］YANG Z, ZHOU J, YAN X, et al.Image enhancement based on fractional differentials ［J］. Journal of Computer-Aided Design and Computer Graphics, 2008,20(3):343-348.(杨柱中,周激流,晏祥玉,等.基于分数阶微分的图像增强［J］.计算机辅助设计与图形学学报,2008,20(3):343-348.)
［9］YANG Z, ZHOU J, HUANG M, et al.Edge detection based on fractional differential ［J］. Journal of Sichuan University: Engineering Science Edition, 2008,40(1):152-157.(杨柱中,周激流,黄梅,等.基于分数阶微分的边缘检测［J］.四川大学学报：工程科学版, 2008,40(1):152-157.)
［10］PU Y, WANG W X, ZHOU J, et al.Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation ［J］. Science in China Series F: Information Sciences, 2008,51(9):1319-1339.
［11］PU Y, ZHOU J, YUAN X. Fractional differential mask: a fractional differential-based approach for multi-scale texture enhancement ［J］. IEEE Transactions on Image Processing, 2010,19(2):491-511.
［12］HUANG G, PU Y, CHEN Q, et al.Research on image denoising based on fractional order integral ［J］. Systems Engineering and Electronics, 2011,33(11):925-932.(黄果,蒲亦非,陈庆利,等.基于分数阶积分的图像去噪［J］.系统工程与电子技术,2011,33(11):925-932.)
［13］YANG Z, LANG F. The construction of fractional differential gradient operator ［J］. Journal of Computational Information Systems, 2011,7(12):4328-4342.
［14］WANG G, MOU J, YANG Z. Noise detection algorithm based on fractional differential gradient ［J］. Journal of Sichuan University: Natural Science Edition, 2014,51(3):483-488.(王刚,牟剑平,杨柱中.基于分数阶微分梯度的噪声检测算法［J］.四川大学学报:自然科学版,2014,51(3):483-488.)

[1]	杨鼎康黄帅王顺利翟鹏李一丹张立华. 基于对抗生成网络和网络集成的面部表情识别方法EE-GAN[J]. 计算机应用, 0, (): 0-0.
[2]	秦庭威赵鹏程秦品乐曾建朝柴锐黄永琦. 基于残差注意力机制的点云配准算法[J]. 计算机应用, 0, (): 0-0.
[3]	裴仪瑶, 郭会明, 张丹普, 陈文博. 基于定位不确定性的鲁棒3D目标检测方法[J]. 计算机应用, 2021, 41(10): 2979-2984.
[4]	许学斌, 张佳达, 刘伟, 路龙宾, 赵雨晴. 融合空间和通道特征的高精度乳腺癌分类方法[J]. 计算机应用, 2021, 41(10): 3025-3032.
[5]	汪虹余, 张彧, 杨恒, 穆楠. 基于蚁群优化算法的弱光图像显著性目标检测[J]. 计算机应用, 2021, 41(10): 2970-2978.
[6]	魏淳武, 赵涓涓, 唐笑先, 强彦. 基于多时期蒸馏网络的随访数据知识提取方法[J]. 计算机应用, 2021, 41(10): 2871-2878.
[7]	李自强, 王正勇, 陈洪刚, 李林怡, 何小海. 基于外观和动作特征双预测模型的视频异常行为检测[J]. 计算机应用, 2021, 41(10): 2997-3003.
[8]	张怡孙永荣赵科东李华曾庆化. 空中加油场景下的目标联合检测跟踪算法[J]. 计算机应用, 0, (): 0-0.
[9]	马峻姚震徐翠锋陈寿宏. 基于改进PP-YOLO和Deep-SORT的多无人机实时跟踪算法[J]. 计算机应用, 0, (): 0-0.
[10]	贺怀清闫建青惠康华. 基于深度残差网络的轻量级人脸识别方法[J]. 计算机应用, 0, (): 0-0.
[11]	王贺兵, 张春梅. 基于非对称卷积-压缩激发-次代残差网络的人脸关键点检测[J]. 计算机应用, 2021, 41(9): 2741-2747.
[12]	张笑非, 杨阳, 黄佳进, 钟宁. 基于度中心性的认知特征选择方法[J]. 计算机应用, 2021, 41(9): 2767-2772.
[13]	姬张建, 任兴旺. 带旋转与尺度估计的全卷积孪生网络目标跟踪算法[J]. 计算机应用, 2021, 41(9): 2705-2711.
[14]	闫钧华侯平张寅吕向阳马越王高飞. 基于多尺度多分类器卷积神经网络的混合失真类型判定方法 [J]. 计算机应用, 0, (): 0-0.
[15]	李福海蒋慕蓉杨磊谌俊毅. 基于生成对抗网络的梯度引导太阳斑点图像去模糊方法[J]. 计算机应用, 0, (): 0-0.

用噪声检测算法改进理想低通滤波器

Improving ideal low-pass filter with noise detection algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics