图像处理的中值滤波算法优化与实现
摘要
随着计算机技术的不断发展,数字图像处理技术越来越得到了更强烈的重视和更广泛的应用。许多新理论和方法也不断涌现出来。在实际应用过程中,由于通信信道和设备、环境等因素干扰,图像信号在产生、传输和记录过程中,经常会受到各种噪声的干扰。数字图像中的原始信号总是和噪声共存的。由于噪声严重地影响了图像的视觉效果,因此在进行边缘检测、图像分割、特征提取、模式识别等工作之前,采用适当的方法减少噪声,即对图像噪声进行滤波,是一项非常重要的预处理步骤。所以使用滤波算法滤除噪声一直以来都是图像处理领域研究的热点。对图像滤波的要求是,既能滤除图像中的噪声又能保持图像的细节。由于噪声和图像细节的混叠,因此在图像滤波中,图像的降噪与细节的保留往往是一对矛盾。图像滤波是图像处理中非常重要的技术环节,至今仍是图像处理领域的研究热点
本文首先介绍了数字图像处理以及中值滤波的基本概念,并对基本的图像滤波技术进行了研究。然后本文主要针对中值滤波算法在图像降噪与保护细节上的缺陷,根据不同的方向提出了两种改进算法。一种是根据多级中值滤波提出的基于方向自适应多级中值滤波算法。作者希望通过加强对方向的判断,更好地保护图像的纹理细节。另一种是根据极值中值滤波提出的基于阈值与比值的极值中值滤波算法。作者首先希望通过对极值点更好的分类,保护图像的细节,然后又希望通过对比值的判断使算法更好的适应实际中的噪声,而不仅仅是理论模型。
With the continuous development of computer technology, digital image processing technology has increasingly gained more attentions and is more widely applied. Many new theories and methods have been emerged continuously. Image signal is often interfered by many kinds of noises when it is produced, transmitted and recorded in practical application processes because of the communication channels and equipment, environmental factors such as interference. The original signal in digital image is always accompanied by noises The quality of image must be improved by filtering technology before edge detection, image segmentation, feature extraction, pattern recognition and so on. Image filtering is expected to both reduce the noise and keep the delicate details. But it is difficult to design such a good filter because of the overlapping of noise and image details. Image filtering is a very important and hot research field in image processing.
Firstly, this thesis discusses some fundamental concepts in digital image processing and median filtering. Secondly, to resolve the contradiction of removing noise and holding image details in median filtering simultaneously, this thesis presents two modified algorithms in different ways. One is direction based adaptive multistage median filtering. The author hopes the details can be protected better through judging the directions of the details. The other is extremum median filtering based on threshold and ratio. The author hopes the details can be kept better by classifying noise more accurately and this algorithm stands the noise brought in our real life better, not only some models in theory.
本文首先介绍了数字图像处理以及中值滤波的基本概念,并对基本的图像滤波技术进行了研究。然后本文主要针对中值滤波算法在图像降噪与保护细节上的缺陷,根据不同的方向提出了两种改进算法。一种是根据多级中值滤波提出的基于方向自适应多级中值滤波算法。作者希望通过加强对方向的判断,更好地保护图像的纹理细节。另一种是根据极值中值滤波提出的基于阈值与比值的极值中值滤波算法。作者首先希望通过对极值点更好的分类,保护图像的细节,然后又希望通过对比值的判断使算法更好的适应实际中的噪声,而不仅仅是理论模型。
With the continuous development of computer technology, digital image processing technology has increasingly gained more attentions and is more widely applied. Many new theories and methods have been emerged continuously. Image signal is often interfered by many kinds of noises when it is produced, transmitted and recorded in practical application processes because of the communication channels and equipment, environmental factors such as interference. The original signal in digital image is always accompanied by noises The quality of image must be improved by filtering technology before edge detection, image segmentation, feature extraction, pattern recognition and so on. Image filtering is expected to both reduce the noise and keep the delicate details. But it is difficult to design such a good filter because of the overlapping of noise and image details. Image filtering is a very important and hot research field in image processing.
Firstly, this thesis discusses some fundamental concepts in digital image processing and median filtering. Secondly, to resolve the contradiction of removing noise and holding image details in median filtering simultaneously, this thesis presents two modified algorithms in different ways. One is direction based adaptive multistage median filtering. The author hopes the details can be protected better through judging the directions of the details. The other is extremum median filtering based on threshold and ratio. The author hopes the details can be kept better by classifying noise more accurately and this algorithm stands the noise brought in our real life better, not only some models in theory.
引文
[1]冈萨雷斯,《数字图像处理第二版中文版》[M],电子工业出版社,北京,2007.4
[2]马学磊,《基于噪声点检测的中值滤波图像去噪算法》[D],西安电子科技大学硕士学位论文,2008.7
[3]Jaffray J. Bayesian Update and Belief Function [J]. IEEE Trans. On SMC,1992, 22(5):1144-115
[4]Klein L A. A Boolean Algebra Approach to Multiple Sensor Voting Fusion[J].IEEE Trans. Areosp. Electron. Syst.,1993,29(1):317-327
[5]周杰,《应用于图像处理的中值滤波改进算法》[D],北京邮电大学硕士学位论文,2007.3
[6]Tao Chen and Hong Ren Wu, "Adaptive Impulse Detection Using Center-Weighted Median Filters" IEEE signal processing letters,2001,8(1):1-3
[7]M. Emin Yuksel, "A Hybrid Neuro-Fuzzy Filter for Edge Preserving Restoration of images corrupted by impulse noise", IEEE TRANS,2006,15(4):928-936
[8]秦鹏,《基于开关中值滤波的图象细节保护滤波器的研究》[D],天津大学硕士学位论文,2004.7
[9]Pei-Eng Ng and Kai-kuang Ma "A Switching Median Filter with Boundary Discriminative Noise Detection for Extremely Corrupted Images", IEEE TRANS,2006,15(6):1506-1516
[10]V. Ponomaryov, A. Rosales_Silva and V. Golikov, "Adaptive and vector directional processing applied to video color images", ELECTRONICS LETTERS,2006,42(11):1-2
[11]索俊祺,《一种新的基于中值滤波的优化滤波算法》[D],北京邮电大学硕士学位论文,2010.3
[12]V. Ponomaryov, F. Gallegos-Funes and A. Rosales-Silva, "Adaptive multi-channel non-parametric median M-type K-nearest neighbour filter to remove impulse noise from color images", Electronic letters,2004,40(13):1-2
[13]Xiaoyin Xu, Eric L. Miller and Mansoor Sarhadi, "Adaptive two-pass order filter to remove impulse noise in highly corrupted images", IEEE Trans,2004, 13(2):238-247
[14]胡桂珍,《基于数字图像处理的车牌识别系统研究》[D],西南交通大学硕士学位论文,2009.7
[15]S. Zhang's, M. A. Karim. "A new impulse detector for switching median filters," IEEE Signal Proc. Lett.2002,9:360-363
[16]X. D. Jiang, "Image detail-preserving filter for impulsive noise attenuation," IEEE Proc.-Vis. Image Signal Process,2003,150(3):179-185
[17]李冬,《基于FPGA的数字图像处理的研究》[D],安徽理工大学硕士学位论文,2009.7
[18]张桂华,《图像处理中的中值滤波的实现.佳木斯大学学报(自然科学版)》[D].2007年7月,第25卷：500-502页
[19]Wenbin Luo, "An efficient detail-preserving approach for removing impulse noise in images",IEEE signal processing letters,2006,13(7):413-416
[20]Raymond H. Chan, Chen Hu and Mila Nikolova, "An iterative procedure for removing random-valued impulse noise", IEEE signal processing letters,2004, 11 (12):921-924
[21]杨海军,《基于方向信息测度的自适应多级中值滤波器》[D],电子与信息学报.2001年12月,第23卷：1326-1332页
[22]Jyh-Yeong Chang and Jia-Lin Chen, "Classifier-Augmented median filter for image restoration", IEEE Trans,2004,53(2):351-356
[23]D. Q. Yuan and Y.-H. Tan, "Difference-type noise detector for adaptive median filter", electronic letter,2006,42(8):1-2
[24]荀中魁,《一种基于极值的自适应中值滤波算法》[D],红外与激光工程.2005年2月,第34卷：98-101页
[25]Igor Aizenberg and Constantine Butakoff, "Efficient impulse detector based on rank-order criteria", IEEE signal processing letters,2004, 11(3):363-366
[26]F. J. Gallegos-Funes, V. I. Ponomaryov and L. Nino-de-Rivero, "Median M-type K-nearest neighbour filter to remove impulse noise from corrupted images", electronic letter,2002,38(15):786-787
[27]邢藏菊,《一种基于极值中值的新型滤波算法》[D],中国图像图形学报.2001年6月,第6卷：533-536页
[28]Ioannis Andreadis and Gerasimos Louverdis, "Real-time adaptive image impulse noise suppression", IEEE Trans,2004,53(3):798-806
[29]Raymond H. Chan, Chung-Wa Ho and Mila Nikolova, "Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization" IEEE Trans,2005,14(10):1479-1485
[2]马学磊,《基于噪声点检测的中值滤波图像去噪算法》[D],西安电子科技大学硕士学位论文,2008.7
[3]Jaffray J. Bayesian Update and Belief Function [J]. IEEE Trans. On SMC,1992, 22(5):1144-115
[4]Klein L A. A Boolean Algebra Approach to Multiple Sensor Voting Fusion[J].IEEE Trans. Areosp. Electron. Syst.,1993,29(1):317-327
[5]周杰,《应用于图像处理的中值滤波改进算法》[D],北京邮电大学硕士学位论文,2007.3
[6]Tao Chen and Hong Ren Wu, "Adaptive Impulse Detection Using Center-Weighted Median Filters" IEEE signal processing letters,2001,8(1):1-3
[7]M. Emin Yuksel, "A Hybrid Neuro-Fuzzy Filter for Edge Preserving Restoration of images corrupted by impulse noise", IEEE TRANS,2006,15(4):928-936
[8]秦鹏,《基于开关中值滤波的图象细节保护滤波器的研究》[D],天津大学硕士学位论文,2004.7
[9]Pei-Eng Ng and Kai-kuang Ma "A Switching Median Filter with Boundary Discriminative Noise Detection for Extremely Corrupted Images", IEEE TRANS,2006,15(6):1506-1516
[10]V. Ponomaryov, A. Rosales_Silva and V. Golikov, "Adaptive and vector directional processing applied to video color images", ELECTRONICS LETTERS,2006,42(11):1-2
[11]索俊祺,《一种新的基于中值滤波的优化滤波算法》[D],北京邮电大学硕士学位论文,2010.3
[12]V. Ponomaryov, F. Gallegos-Funes and A. Rosales-Silva, "Adaptive multi-channel non-parametric median M-type K-nearest neighbour filter to remove impulse noise from color images", Electronic letters,2004,40(13):1-2
[13]Xiaoyin Xu, Eric L. Miller and Mansoor Sarhadi, "Adaptive two-pass order filter to remove impulse noise in highly corrupted images", IEEE Trans,2004, 13(2):238-247
[14]胡桂珍,《基于数字图像处理的车牌识别系统研究》[D],西南交通大学硕士学位论文,2009.7
[15]S. Zhang's, M. A. Karim. "A new impulse detector for switching median filters," IEEE Signal Proc. Lett.2002,9:360-363
[16]X. D. Jiang, "Image detail-preserving filter for impulsive noise attenuation," IEEE Proc.-Vis. Image Signal Process,2003,150(3):179-185
[17]李冬,《基于FPGA的数字图像处理的研究》[D],安徽理工大学硕士学位论文,2009.7
[18]张桂华,《图像处理中的中值滤波的实现.佳木斯大学学报(自然科学版)》[D].2007年7月,第25卷：500-502页
[19]Wenbin Luo, "An efficient detail-preserving approach for removing impulse noise in images",IEEE signal processing letters,2006,13(7):413-416
[20]Raymond H. Chan, Chen Hu and Mila Nikolova, "An iterative procedure for removing random-valued impulse noise", IEEE signal processing letters,2004, 11 (12):921-924
[21]杨海军,《基于方向信息测度的自适应多级中值滤波器》[D],电子与信息学报.2001年12月,第23卷：1326-1332页
[22]Jyh-Yeong Chang and Jia-Lin Chen, "Classifier-Augmented median filter for image restoration", IEEE Trans,2004,53(2):351-356
[23]D. Q. Yuan and Y.-H. Tan, "Difference-type noise detector for adaptive median filter", electronic letter,2006,42(8):1-2
[24]荀中魁,《一种基于极值的自适应中值滤波算法》[D],红外与激光工程.2005年2月,第34卷：98-101页
[25]Igor Aizenberg and Constantine Butakoff, "Efficient impulse detector based on rank-order criteria", IEEE signal processing letters,2004, 11(3):363-366
[26]F. J. Gallegos-Funes, V. I. Ponomaryov and L. Nino-de-Rivero, "Median M-type K-nearest neighbour filter to remove impulse noise from corrupted images", electronic letter,2002,38(15):786-787
[27]邢藏菊,《一种基于极值中值的新型滤波算法》[D],中国图像图形学报.2001年6月,第6卷：533-536页
[28]Ioannis Andreadis and Gerasimos Louverdis, "Real-time adaptive image impulse noise suppression", IEEE Trans,2004,53(3):798-806
[29]Raymond H. Chan, Chung-Wa Ho and Mila Nikolova, "Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization" IEEE Trans,2005,14(10):1479-1485