基于小波变换的图像增强方法研究
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摘要
图像增强包含图像去噪和图像边缘增强两个方面,现有的图像增强方法有很多种,有传统的灰度线性变换,直方图修正,以及一些图像边缘锐化算子等,都能对图像进行相应区域的增强,但是对于图像噪声,这些图像增强方法在增强图像的对比度的时候同时也会增强图像噪声。小波分析的多分辨率分析能够多尺度提取图像边缘特征,能在各个尺度把图像噪声和图像边缘信息区分开来,所以小波分析在图像增强方面具有很大优势。本文主要研究基于小波变换的图像增强方法。主要工作如下：
(1)对传统的图像去噪算法进行研究,通过实验分析这些算法在去噪过程中的问题,然后研究了小波系数相关去噪方法,实验结果表明,该算法可以有效的滤除噪声,保持了图像的边缘,改善了图像的视觉效果。
(2)对图像增强方法展开研究.首先对传统的图像边缘锐化算子进行研究,分析了各种边缘锐化算子的特点,主要研究了小波多尺度边缘检测相对各种单分辨率边缘检测的优点。文章研究了基于小波变换的改进的Sobel算子图像增强算法,一种改进的小波边缘检测算法和一种改进的小波系数相关去噪算法。实验结果表明,三种方法都能在噪声干扰下有效的增强图像感兴趣区域,具有很好的抗噪性,图像增强效果理想。
Image enhancement includes image denosing and edge enhancement. Many traditional methods in current of image enhancement can enhance the gray constrast of intresting region, such as liner transform, histogram modification, and some edge sharpening arithmetics, yet noise is enhanced. As wavelet transform can analysis an image from multiresolution, which can differentiate noise and edge, wavelet transform has great advantage in image enhancement..In this paper, image enhancement based on wavelet transform is mainly studied. Work has been done as follow.
Firstly,the tranditional methood of image denoising with contrastive analysis besed on the data of the simulation experiment.and a method of wavelet correlation coefficient image denoising are studied. The simulation results show that the method studied can filter off the high frequency noise effectively, preserve the edge of image, and improve the visual affect as well.
Sencondly, reseach of the traditional edge sharpening methods is done,the advantage of wavelet edge detection is mainly studied compared to other edge detection methods which are sigleresolution. Three methods of noisy image enhancement are studied, one is an improved sobel operator image enhancing based on wavelet transform, the other one is wavelet edge detection based on inter-scale phase correlation, the third one is an improved method of coefficient correlation denoising. The simulation results show noise immunity of all the three methods as the intersting region is enhanced and image visual affect is improved as well under the interference of noise.
(1)对传统的图像去噪算法进行研究,通过实验分析这些算法在去噪过程中的问题,然后研究了小波系数相关去噪方法,实验结果表明,该算法可以有效的滤除噪声,保持了图像的边缘,改善了图像的视觉效果。
(2)对图像增强方法展开研究.首先对传统的图像边缘锐化算子进行研究,分析了各种边缘锐化算子的特点,主要研究了小波多尺度边缘检测相对各种单分辨率边缘检测的优点。文章研究了基于小波变换的改进的Sobel算子图像增强算法,一种改进的小波边缘检测算法和一种改进的小波系数相关去噪算法。实验结果表明,三种方法都能在噪声干扰下有效的增强图像感兴趣区域,具有很好的抗噪性,图像增强效果理想。
Image enhancement includes image denosing and edge enhancement. Many traditional methods in current of image enhancement can enhance the gray constrast of intresting region, such as liner transform, histogram modification, and some edge sharpening arithmetics, yet noise is enhanced. As wavelet transform can analysis an image from multiresolution, which can differentiate noise and edge, wavelet transform has great advantage in image enhancement..In this paper, image enhancement based on wavelet transform is mainly studied. Work has been done as follow.
Firstly,the tranditional methood of image denoising with contrastive analysis besed on the data of the simulation experiment.and a method of wavelet correlation coefficient image denoising are studied. The simulation results show that the method studied can filter off the high frequency noise effectively, preserve the edge of image, and improve the visual affect as well.
Sencondly, reseach of the traditional edge sharpening methods is done,the advantage of wavelet edge detection is mainly studied compared to other edge detection methods which are sigleresolution. Three methods of noisy image enhancement are studied, one is an improved sobel operator image enhancing based on wavelet transform, the other one is wavelet edge detection based on inter-scale phase correlation, the third one is an improved method of coefficient correlation denoising. The simulation results show noise immunity of all the three methods as the intersting region is enhanced and image visual affect is improved as well under the interference of noise.
引文
[1]周新伦.数字图像处理[M].北京：国防工业出版社,1986
[2]K R castleman著,朱志刚等译.数字图像处理[M].北京：电子工业出版社,1988
[3]王国权,仲伟波.灰度图像增强算法的改进与实现研究[J].计算机应用研究,2004,21(12)：175～176.
[4]吴颖谦.基于小波变换的低对比度图像增强[J].红外与激光工程,2003,2(1)：4～7
[5]方勇.基于软闭值的小波图像增强方法[J].计算机工程与应用,2002,23：16～19
[6]章毓晋.图像工程(上册)图像处理和分析[M].北京：清华大学出版社,2004
[7]C Taswell. The what, how, and why of wavelet shrinkage de-noising [J]. Computering in Science & Engineering,2000,23:12～19
[8]费佩燕,郭宝龙.二进小波变换的图像去噪[J].计算机工程与应用,2003,8：9～12
[9]尚晓清,王军锋,宋国乡.一种基于自适应阈值的图像去噪新方法[J].计算机科学,2003,30(9)：70～71
[10]陈海峰,王伟.一种具有自适应阈值的小波收缩去噪方法明[J].计算机仿真2005,22(5)：107～109
[11]Jansen M. Noise reduction by wavelet thresholding[M]. Springer Verlag, Lecture notes in Statistics,2001
[12]谢杰成,张大力,徐文立.小波图象去噪综述[J].中国图象图形学报：A辑,2002,7(3)：209～217
[13]赵凡.彩色图象的一种小波去噪方法[J].陕西工学院学报：自然科学版.2004,20(1)：41～43
[14]周文锐,狄红卫,任观就.基于小波变换的图像去噪优化算法[J].计算机工程与设计,2005,26(12)：3244～3246
[15]王红梅,李言俊,张科.基于二进小波变换的图像去噪方法[J].红外技术,2006,28(7)：404～407
[16]Nason G P, Silverman B W. The Stationary wavelet transform and some statistiscal applications[M]. Lecture Notes in statistics, Berlin:Springer Verlag, 1995,281～299
[17]Zhang YongXian, ChenMei. A Thresholding Automatic Setting Method for Edge Detection Based on Wavelet Transform[J], Microcomputer,2007,3: 286～287
[18]Mallat S and S Zhong, Characterization of Signals from Multiscale Edges[J], IEEE Trans. PAMI,1992,14(7):710～732
[19]ACCAME M, FRANCESCO G B. Edge detection by point classification of canny filtered images[J]. Signal Processing,1997,60(1):11～22
[20]Rafael C Gonzalez, Richard E Woods, Digital Image Processing Using MATLAB[M]. Bejing:Publishing House of Electronics Industry.2007
[21]Mallat S and Hwang W L. Sigularity detection and processing with wavelets[J]. IEEE Trans Info Theory,1992,38(2):617～643
[22]Wang X H, Robert S H Istepanian, Yong Hua Song. Application of wavelet modulus maxima in microarray spots recognition[J], IEEE transaction on Nanobiosicence,2003,2(7):1～24
[23]葛哲学,鲨威.小波分析理论与MATLAB R2007实现[M].北京：电子工业出版社,2007
[24]魏明果.实用小波分析[M].北京：北京理工大学出版社,2005
[25]彭玉华.小波变换与工程应用[M].北京：科学出版社,1999
[26]杨福生.小波变换的工程分析与应用[M].北京：科学出版社,1999
[27]刘明才.小波分析与其应用[M].北京：清华大学出版社,2005
[28]陶德元,何小梅.小波变换及其在图像处理中的应用[J],四川人学学报(自然科学版),1994,4：481～488
[29]Dong-June Lee, Seong-Whan Lee. A new methodology forg ray-scale character segmentation and recognition[J]. Ocument Analysis and Recognition,1995, 1045～1050
[30]B L McGlamery. Restoration of turbulence-degraded images[J]. Optical society of America,1967,57(3):293～297
[31]P.F Mueller and G O Reynolds. Image restoration by removal of random media degradations[J]. Optical society of America,1967,57(11):1338～1344
[32]C W Helstrom. Image restoration by the method of least squares[J]. Optical society of America,1967,57(3):297～303
[33]J W Tukey. Exploratory data analysis[M]. Addison-Wesley, Reading, MA, 1971
[34]ANIL K.JAIN. Fundamentals of Digital Image Processing[M].北京：清华大学出版社,2006
[35]S Mallat. A Wavelet Tour of Signal Processing[J]. Academic Press,1998, 127～271
[36]石洪波,马驷良,韩笑.一种基于小波变换图像去噪的新方法[J].吉林大学学报(理学版),2007,45(4)：607～610
[37]S Mallat. A Wavelet Tour of Signal Processing [M]. China Machine press,2003
[38]Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage[J]. IEEE Trans. Inform. Theory,1992,81(3):425～455
[39]Donoho D L, Johnstone I M. Minimax estimation via wavelet shrinkage[J]. Annals of Statistics,1998,26(3):879～921
[40]Hansen M, Yu Bin. Wavelet thresholding via MDL for natural image[J]. IEEE Trans. Inform. Theory,2000,46(5):1778～1788
[41]Donoho D L. De-noising by soft-thresholding[J]. IEEE Trans. Inform. Theory, 1995,41(3):613～627
[42]潘泉,张磊,孟晋丽等.小波滤波方法及应用[M].北京：清华大学出版社,2005
[43]K R Castleman著,朱志刚等译.数字图像处理[M].北京：电子工业出版社,1998.
[44]沈庭芝,方子文.数字图像处理及模式识别[M].北京：北京理工大学出版社,2001
[45]SOBEL I. Camera models and machine perception[M]. CA:Stanford AI Memo, 1970
[46]L G Roberts. Machine Perception of Three-Dimensional Solids in Optical and Electro-Optical Information Processing[J],J T Tippettetal, Eds, MIT Press, Cambridge, MA,1965,159～197
[47]徐建华.图像处理与分析[M].北京：科学出版社,1992
[48]J Canny. A computational approach to edge detection[J], IEEE Trans, PaRem Anal. And Machine Intell, vOl. PAMI,1986,8:639～643
[49]王发牛,梁栋,唐俊等.小波变换模极大值多尺度边缘检测[J].仪器仪表学报(增刊),2006,27(6)：2147～2149
[50]Mallat S, Zhong S. Characterization of signals from multiscale edges[J]. IEEE Trans PAMI,1992,14(7):710～732
[51]朱秀昌,刘峰,胡栋.数字图像处理与图像通信[M].北京：北京邮电大学出 版社,2002
[52]焦书兰等.人类视觉系统获取颜色恒常性的认知操作特性[J].心理学报,1997,27(2)：113～120
[53]王保平,刘怀亮,李南京等.一种新的自适应图像模糊增强算法[J].西安电子科技大学学报(自然科学版),2005,32(2)：307～313.
[54]李云,刘学诚.基于小波变换的图像增强算法研究[J].计算机应用与软件,2008,25(8)：100～103
[55]王小凤,耿国华,郭红波.一种改进的图像增强算法及其应用[J].计算机应用与软件,2008,25(9)：39～40
[56]翟改霞,王春光.基于小波多分辨率分析的图像模糊增强算法的研究与实现[J].计算机应用与软件,2008,25(10)：261～262
[57]Mallat S G. Muhifrequency channel decompositions of images and wavelet models[J]. IEEE Trans,1989,37:2091～2110.
[58]徐军,梁昌洪,张建奇.一种红外图像增强的新方法[J].西安电子科技大学学报(自然科学版),2000,27(5)：547～549
[59]向健勇,徐军.一种实用的红外图像分割算法研究[J].西安电子科技大学学报(自然科学版),1997,24(3)：416～420
[60]孙全,叶秀清,顾伟康.一种基于图像分割的增强算法[J].中国图像图形学报,1999,4(A)(5)：409～413
[2]K R castleman著,朱志刚等译.数字图像处理[M].北京：电子工业出版社,1988
[3]王国权,仲伟波.灰度图像增强算法的改进与实现研究[J].计算机应用研究,2004,21(12)：175～176.
[4]吴颖谦.基于小波变换的低对比度图像增强[J].红外与激光工程,2003,2(1)：4～7
[5]方勇.基于软闭值的小波图像增强方法[J].计算机工程与应用,2002,23：16～19
[6]章毓晋.图像工程(上册)图像处理和分析[M].北京：清华大学出版社,2004
[7]C Taswell. The what, how, and why of wavelet shrinkage de-noising [J]. Computering in Science & Engineering,2000,23:12～19
[8]费佩燕,郭宝龙.二进小波变换的图像去噪[J].计算机工程与应用,2003,8：9～12
[9]尚晓清,王军锋,宋国乡.一种基于自适应阈值的图像去噪新方法[J].计算机科学,2003,30(9)：70～71
[10]陈海峰,王伟.一种具有自适应阈值的小波收缩去噪方法明[J].计算机仿真2005,22(5)：107～109
[11]Jansen M. Noise reduction by wavelet thresholding[M]. Springer Verlag, Lecture notes in Statistics,2001
[12]谢杰成,张大力,徐文立.小波图象去噪综述[J].中国图象图形学报：A辑,2002,7(3)：209～217
[13]赵凡.彩色图象的一种小波去噪方法[J].陕西工学院学报：自然科学版.2004,20(1)：41～43
[14]周文锐,狄红卫,任观就.基于小波变换的图像去噪优化算法[J].计算机工程与设计,2005,26(12)：3244～3246
[15]王红梅,李言俊,张科.基于二进小波变换的图像去噪方法[J].红外技术,2006,28(7)：404～407
[16]Nason G P, Silverman B W. The Stationary wavelet transform and some statistiscal applications[M]. Lecture Notes in statistics, Berlin:Springer Verlag, 1995,281～299
[17]Zhang YongXian, ChenMei. A Thresholding Automatic Setting Method for Edge Detection Based on Wavelet Transform[J], Microcomputer,2007,3: 286～287
[18]Mallat S and S Zhong, Characterization of Signals from Multiscale Edges[J], IEEE Trans. PAMI,1992,14(7):710～732
[19]ACCAME M, FRANCESCO G B. Edge detection by point classification of canny filtered images[J]. Signal Processing,1997,60(1):11～22
[20]Rafael C Gonzalez, Richard E Woods, Digital Image Processing Using MATLAB[M]. Bejing:Publishing House of Electronics Industry.2007
[21]Mallat S and Hwang W L. Sigularity detection and processing with wavelets[J]. IEEE Trans Info Theory,1992,38(2):617～643
[22]Wang X H, Robert S H Istepanian, Yong Hua Song. Application of wavelet modulus maxima in microarray spots recognition[J], IEEE transaction on Nanobiosicence,2003,2(7):1～24
[23]葛哲学,鲨威.小波分析理论与MATLAB R2007实现[M].北京：电子工业出版社,2007
[24]魏明果.实用小波分析[M].北京：北京理工大学出版社,2005
[25]彭玉华.小波变换与工程应用[M].北京：科学出版社,1999
[26]杨福生.小波变换的工程分析与应用[M].北京：科学出版社,1999
[27]刘明才.小波分析与其应用[M].北京：清华大学出版社,2005
[28]陶德元,何小梅.小波变换及其在图像处理中的应用[J],四川人学学报(自然科学版),1994,4：481～488
[29]Dong-June Lee, Seong-Whan Lee. A new methodology forg ray-scale character segmentation and recognition[J]. Ocument Analysis and Recognition,1995, 1045～1050
[30]B L McGlamery. Restoration of turbulence-degraded images[J]. Optical society of America,1967,57(3):293～297
[31]P.F Mueller and G O Reynolds. Image restoration by removal of random media degradations[J]. Optical society of America,1967,57(11):1338～1344
[32]C W Helstrom. Image restoration by the method of least squares[J]. Optical society of America,1967,57(3):297～303
[33]J W Tukey. Exploratory data analysis[M]. Addison-Wesley, Reading, MA, 1971
[34]ANIL K.JAIN. Fundamentals of Digital Image Processing[M].北京：清华大学出版社,2006
[35]S Mallat. A Wavelet Tour of Signal Processing[J]. Academic Press,1998, 127～271
[36]石洪波,马驷良,韩笑.一种基于小波变换图像去噪的新方法[J].吉林大学学报(理学版),2007,45(4)：607～610
[37]S Mallat. A Wavelet Tour of Signal Processing [M]. China Machine press,2003
[38]Donoho D L, Johnstone I M. Ideal spatial adaptation by wavelet shrinkage[J]. IEEE Trans. Inform. Theory,1992,81(3):425～455
[39]Donoho D L, Johnstone I M. Minimax estimation via wavelet shrinkage[J]. Annals of Statistics,1998,26(3):879～921
[40]Hansen M, Yu Bin. Wavelet thresholding via MDL for natural image[J]. IEEE Trans. Inform. Theory,2000,46(5):1778～1788
[41]Donoho D L. De-noising by soft-thresholding[J]. IEEE Trans. Inform. Theory, 1995,41(3):613～627
[42]潘泉,张磊,孟晋丽等.小波滤波方法及应用[M].北京：清华大学出版社,2005
[43]K R Castleman著,朱志刚等译.数字图像处理[M].北京：电子工业出版社,1998.
[44]沈庭芝,方子文.数字图像处理及模式识别[M].北京：北京理工大学出版社,2001
[45]SOBEL I. Camera models and machine perception[M]. CA:Stanford AI Memo, 1970
[46]L G Roberts. Machine Perception of Three-Dimensional Solids in Optical and Electro-Optical Information Processing[J],J T Tippettetal, Eds, MIT Press, Cambridge, MA,1965,159～197
[47]徐建华.图像处理与分析[M].北京：科学出版社,1992
[48]J Canny. A computational approach to edge detection[J], IEEE Trans, PaRem Anal. And Machine Intell, vOl. PAMI,1986,8:639～643
[49]王发牛,梁栋,唐俊等.小波变换模极大值多尺度边缘检测[J].仪器仪表学报(增刊),2006,27(6)：2147～2149
[50]Mallat S, Zhong S. Characterization of signals from multiscale edges[J]. IEEE Trans PAMI,1992,14(7):710～732
[51]朱秀昌,刘峰,胡栋.数字图像处理与图像通信[M].北京：北京邮电大学出 版社,2002
[52]焦书兰等.人类视觉系统获取颜色恒常性的认知操作特性[J].心理学报,1997,27(2)：113～120
[53]王保平,刘怀亮,李南京等.一种新的自适应图像模糊增强算法[J].西安电子科技大学学报(自然科学版),2005,32(2)：307～313.
[54]李云,刘学诚.基于小波变换的图像增强算法研究[J].计算机应用与软件,2008,25(8)：100～103
[55]王小凤,耿国华,郭红波.一种改进的图像增强算法及其应用[J].计算机应用与软件,2008,25(9)：39～40
[56]翟改霞,王春光.基于小波多分辨率分析的图像模糊增强算法的研究与实现[J].计算机应用与软件,2008,25(10)：261～262
[57]Mallat S G. Muhifrequency channel decompositions of images and wavelet models[J]. IEEE Trans,1989,37:2091～2110.
[58]徐军,梁昌洪,张建奇.一种红外图像增强的新方法[J].西安电子科技大学学报(自然科学版),2000,27(5)：547～549
[59]向健勇,徐军.一种实用的红外图像分割算法研究[J].西安电子科技大学学报(自然科学版),1997,24(3)：416～420
[60]孙全,叶秀清,顾伟康.一种基于图像分割的增强算法[J].中国图像图形学报,1999,4(A)(5)：409～413