结合不可分小波分析和曲线拟合方法的图像对比度增强
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摘要
传统的图像对比度增强技术容易造成图像的局部区域过分增强或增强不足等问题,因此提出了一种结合不可分小波分析和曲线拟合的图像对比度增强方法。该方法以标准清晰图像的均值和标准差作为图像对比度增强的理想目标,首先对同一目标的标准清晰图像和低对比度图像进行四通道不可分小波分解,然后利用曲线拟合的预测能力得出分解后具有相同频率通道和相同方向子图像的均值之间及标准差之间的函数关系,通过该函数关系和直方图匹配公式即可得到理想的增强后的子图像,最后将这些新的子图像进行不可分小波重构,并采用清晰度、标准差指标对图像对比度增强结果进行评价。实验结果表明,所提方法所得低对比度图像的增强效果较好,且得到了高清晰度图像。
Traditional image contrast enhancement technology can easily cause over-enhancement or under-enhancement in the local area of images. Thus, an image contrast enhancement method combining nonseparable wavelet analysis and curve fitting is proposed. The method uses mean and standard deviation of the standard clear image as the ideal target for image contrast enhancement. Firstly, four-channel nonseparable wavelet is used to decompose standard clear image and low contrast image of the same target. Next, the predictive ability of curve fitting is used to get the relationships among mean values and the relationships among standard deviations of sub-images with same frequency channel and same direction. Then, the ideal enhanced sub-images can be obtained by the functional relationships and histogram matching formulas. Finally, these new sub-images are reconstructed by nonseparable wavelet. The performance of the method is evaluated with clarity and standard deviation. The experimental results show that the proposed method has a good enhancement effect for low contrast image, and can obtain high definition images.
Traditional image contrast enhancement technology can easily cause over-enhancement or under-enhancement in the local area of images. Thus, an image contrast enhancement method combining nonseparable wavelet analysis and curve fitting is proposed. The method uses mean and standard deviation of the standard clear image as the ideal target for image contrast enhancement. Firstly, four-channel nonseparable wavelet is used to decompose standard clear image and low contrast image of the same target. Next, the predictive ability of curve fitting is used to get the relationships among mean values and the relationships among standard deviations of sub-images with same frequency channel and same direction. Then, the ideal enhanced sub-images can be obtained by the functional relationships and histogram matching formulas. Finally, these new sub-images are reconstructed by nonseparable wavelet. The performance of the method is evaluated with clarity and standard deviation. The experimental results show that the proposed method has a good enhancement effect for low contrast image, and can obtain high definition images.
引文
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[12]Xu T Y,Hui X W,Lin S.A near infrared finger vein recognition approach based on wavelet grayscale surface matching[J].Laser&Optoelectronics Progress,2016,53(4):041005.-9徐天扬,惠晓威,林森.基于小波灰度曲面的近红外手指静脉识别方法[J].激光与光电子学进展,2016,53(4):041005.
[13]Hong X T,Shi X W,Qian Y X.Optical correlation recognition based on edge detection of wavelet transform[J].Laser&Optoelectronics Progress,2014,51(4):041004.洪雪婷,石晓卫,钱义先.基于小波边缘提取的目标光学相关识别[J].激光与光电子学进展,2014,51(4):041004.
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[16]Liu B,Sun B,Yu F C,et al.Image registration method based on nonseparable wavelet decomposition[J].Computer Engineering,2014,40(10):252-257.刘斌,孙斌,余方超,等.基于不可分小波分解的图像配准方法[J].计算机工程,2014,40(10):252-257.
[17]Li K,Liu B.Multi-focus image fusion based on nonseparable symmetric wavelets[J].Journal of Computer Applications,2012,32(5):1283-1285.李凯,刘斌.基于具有对称性不可分小波的多聚焦图像融合[J].计算机应用,2012,32(5):1283-1285.
[18]Xu C F,Li G K.Practical wavelet method[M].Wuhan:Press of Huazhong University of Science and Technology,2009.徐长发,李国宽.实用小波方法[M].武汉:华中科技大学出版社,2009.
[19]Li Q Y,Wang N C,Yi D Y.Numerical analysis[M].Beijing:Tsinghua University Press,2008.李庆扬,王能超,易大义.数值分析[M].北京:清华大学出版社,2008.
[20]Liu B,Peng J X.Multi-spectral image fusion method based on nonsubsampled three channels nonseparable symmetrical wavelets[J].Acta Electronica Sinica,2011,39(5):1094-1099.刘斌,彭嘉雄.基于非下采样三通道不可分对称小波的多光谱图像融合[J].电子学报,2011,39(5):1094-1099.
[21]Li Z F.Image enhancement method based on wavelet transform[J].Mechanical Engineering&Automation,2009(2):15-17.李正飞.基于小波变换的图像对比度增强技术研究[J].机械工程与自动化,2009(2):15-17.
[22]Yang H T,Chang Y L,Wang J,et al.A new automatic exposure algorithm for video cameras using luminance histogram[J].Acta Optica Sinica,2007,27(5):841-847.杨海涛,常义林,王静,等.一种基于亮度直方图的自动曝光控制方法[J].光学学报,2007,27(5):841-847.
[23]Sang G L,Xuan S B,Zhao B,et al.Fusion algorithm for infrared and visible light images using object extraction and NSCT[J].Computer Engineering and Applications,2013,49(11):172-176.桑高丽,宣士斌,赵波,等.采用目标提取和NSCT的红外与可见光图像融合[J].计算机工程与应用,2013,49(11):172-176.
[24]Liu B,Liu W J,Ma J L.Multi-focus image fusion based on three channel nonseparable symmetrical wavelets[J].Chinese Journal of Scientific Instrument,2012,33(5):1110-1116.刘斌,刘维杰,马嘉利.基于三通道不可分对称小波的多聚焦图像融合[J].仪器仪表学报,2012,33(5):1110-1116.
[25]Wen H B,Bi D Y,Ma S P,et al.Variational Retinex algorithm for infrared image enhancement with staircase effect suppression and detail enhancement[J].Acta Optica Sinica,2016,36(9):0911005.温海滨,毕笃彦,马时平,等.消除阶梯效应与增强细节的变分Retinex红外图像对比度增强算法[J].光学学报,2016,36(9):0911005.
[2]Yu Z M,Zhang Y,Lian F Y,et al.A research of grayscale transformation based on digital image enhancement[J].Electronic Quality,2009(6):18-20.余章明,张元,廉飞宇,等.数字图像对比度增强中灰度变换方法研究[J].电子质量,2009(6):18-20.
[3]Wang R K.Study on the algorithms of image contrast enhancement based on fuzzy theory[D].Taiyuan:North University of China,2014.王睿凯.基于模糊理论的图像对比度增强算法研究[D].太原:中北大学,2014.
[4]Gonzalez R C,Wintz P.Digital image processing[M].Beijing:Publishing House of Electronics Industry,2010.
[5]Cao J L,LüH B,Li G Z.Histogram equalization algorithm based on adaptive local gray level modification[J].Infrared and Laser Engineering,2004,33(5):513-515.曹聚亮,吕海宝,李冠章.基于自适应局部灰度修正的直方图均衡算法[J].红外与激光工程,2004,33(5):513-515.
[6]Han X Z.Study on image enhancement method based on partial differential equation[D].Changchun:Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,2010.韩希珍.基于偏微分方程的图像对比度增强方法研究[D].长春:中国科学院长春光学精密机械与物理研究所,2010.
[7]Zhu L X,Wang P A,Xia D S.Image contrast enhancement by gradient field equalization[J].Journal of Computer-Aided Design&Computer Graphics,2007,19(12):1546-1552.朱立新,王平安,夏德深.基于梯度场均衡化的图像对比度增强[J].计算机辅助设计与图形学学报,2007,19(12):1546-1552.
[8]Ke J,Hou Y Q,Wang D K,et al.An improved algorithm for shape preserving contrast enhancement[J].Acta Photonica Sinica,2009,38(1):214-219.克兢,侯榆青,王大凯,等.一种改进型保持形状的图像对比度增强算法[J].光子学报,2009,38(1):214-219.
[9]Li Q Z,Liu Q.Adaptive enhancement algorithm for low illumination images based on wavelet transform[J].Chinese Journal of Lasers,2015,42(2):0209001.李庆忠,刘清.基于小波变换的低照度图像自适应增强算法[J].中国激光,2015,42(2):0209001.
[10]Feng F,Li C W.Simulation of atmospheric turbulence phase screen based on wavelet analysis[J].Acta Optica Sinica,2017,37(1):0101004.丰帆,李常伟.基于小波分析的大气湍流相位屏模拟[J].光学学报,2017,37(1):0101004.
[11]Liu G J,Tang X L,Huang J H,et al.An image contrast enhancement approach based on fuzzy wavelet[J].Acta Electronica Sinica,2005,33(4):643-646.刘国军,唐降龙,黄剑华,等.基于模糊小波的图像对比度增强算法[J].电子学报,2005,33(4):643-646.
[12]Xu T Y,Hui X W,Lin S.A near infrared finger vein recognition approach based on wavelet grayscale surface matching[J].Laser&Optoelectronics Progress,2016,53(4):041005.-9徐天扬,惠晓威,林森.基于小波灰度曲面的近红外手指静脉识别方法[J].激光与光电子学进展,2016,53(4):041005.
[13]Hong X T,Shi X W,Qian Y X.Optical correlation recognition based on edge detection of wavelet transform[J].Laser&Optoelectronics Progress,2014,51(4):041004.洪雪婷,石晓卫,钱义先.基于小波边缘提取的目标光学相关识别[J].激光与光电子学进展,2014,51(4):041004.
[14]Kovacevic J,Vetterli M.Nonseparable multidimensional perfect reconstruction filter banks and wavelet bases for Rn[J].IEEE Transactions on Information theory,1992,38(2):533-555.
[15]Liu B,Peng J X.Image fusion based on non-separable orthogonal compact supported wavelet[J].Acta Optica Sinica,2004,24(9):1214-1218.刘斌,彭嘉雄.具有紧支撑正交非张量积小波的图像融合[J].光学学报,2004,24(9):1214-1218.
[16]Liu B,Sun B,Yu F C,et al.Image registration method based on nonseparable wavelet decomposition[J].Computer Engineering,2014,40(10):252-257.刘斌,孙斌,余方超,等.基于不可分小波分解的图像配准方法[J].计算机工程,2014,40(10):252-257.
[17]Li K,Liu B.Multi-focus image fusion based on nonseparable symmetric wavelets[J].Journal of Computer Applications,2012,32(5):1283-1285.李凯,刘斌.基于具有对称性不可分小波的多聚焦图像融合[J].计算机应用,2012,32(5):1283-1285.
[18]Xu C F,Li G K.Practical wavelet method[M].Wuhan:Press of Huazhong University of Science and Technology,2009.徐长发,李国宽.实用小波方法[M].武汉:华中科技大学出版社,2009.
[19]Li Q Y,Wang N C,Yi D Y.Numerical analysis[M].Beijing:Tsinghua University Press,2008.李庆扬,王能超,易大义.数值分析[M].北京:清华大学出版社,2008.
[20]Liu B,Peng J X.Multi-spectral image fusion method based on nonsubsampled three channels nonseparable symmetrical wavelets[J].Acta Electronica Sinica,2011,39(5):1094-1099.刘斌,彭嘉雄.基于非下采样三通道不可分对称小波的多光谱图像融合[J].电子学报,2011,39(5):1094-1099.
[21]Li Z F.Image enhancement method based on wavelet transform[J].Mechanical Engineering&Automation,2009(2):15-17.李正飞.基于小波变换的图像对比度增强技术研究[J].机械工程与自动化,2009(2):15-17.
[22]Yang H T,Chang Y L,Wang J,et al.A new automatic exposure algorithm for video cameras using luminance histogram[J].Acta Optica Sinica,2007,27(5):841-847.杨海涛,常义林,王静,等.一种基于亮度直方图的自动曝光控制方法[J].光学学报,2007,27(5):841-847.
[23]Sang G L,Xuan S B,Zhao B,et al.Fusion algorithm for infrared and visible light images using object extraction and NSCT[J].Computer Engineering and Applications,2013,49(11):172-176.桑高丽,宣士斌,赵波,等.采用目标提取和NSCT的红外与可见光图像融合[J].计算机工程与应用,2013,49(11):172-176.
[24]Liu B,Liu W J,Ma J L.Multi-focus image fusion based on three channel nonseparable symmetrical wavelets[J].Chinese Journal of Scientific Instrument,2012,33(5):1110-1116.刘斌,刘维杰,马嘉利.基于三通道不可分对称小波的多聚焦图像融合[J].仪器仪表学报,2012,33(5):1110-1116.
[25]Wen H B,Bi D Y,Ma S P,et al.Variational Retinex algorithm for infrared image enhancement with staircase effect suppression and detail enhancement[J].Acta Optica Sinica,2016,36(9):0911005.温海滨,毕笃彦,马时平,等.消除阶梯效应与增强细节的变分Retinex红外图像对比度增强算法[J].光学学报,2016,36(9):0911005.