基于改进小波阈值函数的医学CT图像降噪
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摘要
针对医学CT图像信噪比低的特点,提出了一种基于软硬阈值的混合函数并结合双树复小波变换的优势对图像进行去噪。首先将图像进行多层分解,然后对分解后的高频子带系数进行新阈值处理,最后通过重构还原图像,经过与其他几种方法的对比,此方法取得了较好的处理效果和峰值信噪比(PSNR)并保留了图像的边缘纹理特征。
According to the characteristics of medical pathology image signal-to-noise ratio is low,this paper proposes a hy-brid based on hard and soft threshold function and combining the advantages of dual tree complex wavelet transform for image denois-ing. First of all,the image is decomposed,then data of each layer undergoes a process with high frequency coefficient threshold,fi-nally,the reconstruction image is restored,Through comparison with several other methods,this method has obtained the good pro-cessing effect and peak signal to noise ratio(PSNR)and preserve the edge of the image texture feature.
According to the characteristics of medical pathology image signal-to-noise ratio is low,this paper proposes a hy-brid based on hard and soft threshold function and combining the advantages of dual tree complex wavelet transform for image denois-ing. First of all,the image is decomposed,then data of each layer undergoes a process with high frequency coefficient threshold,fi-nally,the reconstruction image is restored,Through comparison with several other methods,this method has obtained the good pro-cessing effect and peak signal to noise ratio(PSNR)and preserve the edge of the image texture feature.
引文
[1]张郝.基于小波变换的图像去噪方法研究[D].北京:北京交通大学,2008.ZHAGN Hao.The Research On Image Denoising BasedOn Wavelet Transform[D].Beijing:Beijing Jiaotong Uni-versity,2008.
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[5]Singh H,Kaur L,Singh K.Fractional M-band dual tree complex wavelet transform for digital watermarking[J].Sadhana,2014,39(2):345-361.
[6]杨鑫蕊.改进的小波阈值去噪算法研究[D].哈尔滨:哈尔滨理工大学,2014.YANG Xinrui.The Study On Improve Wavelete ThresholdDenoising Method[D].Harbin:Harbin University of Sci-ence and Technology,2014.
[7]郭巍,张平,陈曦,等.基于双密度双树复数小波变换的合成孔径雷达图像降噪研究[J].电子学报,2009,37(12):2747-2752.GUO Wei,ZHANG Ping,CHEN Xi,et al.Research onSynthetic Aperture Radar Image Denoising With DoubleDensity Dual-Tree Complex Wavelet Transform[J].ActaElectronicasinica,2009,37(12):2747-2752.
[8]刘蕾.基于双树复小波变换的图像去噪[D].北京:北京化工大学,2010.LIU Lei.Image Denoising Based on The Dual Tree Com-plex Wavelet Transform[D].Beijing:Beijing University ofChemical Technology,2010.
[9]李宁,水鹏郎.一种加窗的双重局部维纳滤波图像去噪算法-基于SWT和DTCWT[J].计算机工程与应用,2007,43(28):44-46.LI Ning,SHUI Penglang.Image Desnoising Algorithm ViaDoubly Local Wiener Filtering With Windows Based OnSWT And DTCWT[J].computer Endineering and Appli-cations,2007,43(28):44-46.
[10]田园.基于多尺度小波变换的肿瘤图像纹理分析及其临床应用[D].济南:山东师范大学,2015.TIAN Yuan.The Tumor Texture Analysis And Its Clini-cal Application Based On Multi-Scale Wavelet Transform[D].Jinan:Shandong Normal University,2015.
[11]张国伟.基于小波变换的图像去噪方法研究[D].昆明:昆明理工大学,2014.ZHANG Guowei.The Research On Image DenoisingBased On Wavelet Transform[D].Kunming:KunmingUniversity of Science and Technology,2014.
[12]王芳.基于双数复小波变换的微弱生物医学信号处理及其应用研究[D].重庆:重庆大学,2014.WANG Fang.Application of Dual-Tree Complex Wave-let Transform In Weak Biomedical Signal Processing[D].Chongqing:Chongqing University,2014.
[13]Satheesh S,Prasad K.Medical image denoising usingadaptive threshold based on contourlet transform[J].ar Xiv preprint ar Xiv:1103.4907,2011.
[14]Wang Y,Fu X,Chen L,et al.DTCWT based medicalultrasound images despeckling using LS parameter opti-mization[C]//2013 IEEE International Conference on Im-age Processing.IEEE,2013:805-809.
[15]Liu Y,Jiang M.Image denoising algorithm based onDTCWT and adaptive windows[C]//Computer Scienceand Information Technology(ICCSIT),2010 3rd IEEEInternational Conference on.IEEE,2010,8:207-210.
[2]张文革,刘芳,高新波,等.一种自适应多尺度积阈值的图像去噪算法[J].电子与信息学报,2009,31(8):1780-1784.ZHANG Wenge,LIU Fang,GAO Xinbo,et al.An ImageDenoising Algorithm Using Adaptive Multiscale ProjectsThresholding[J].Journal of Electronics&InformationTechnology,2009,31(8):1780-1784.
[3]Raj V N P,Venkateswarlu T.Denoising of medical imag-es using dual tree complex wavelet transform[J].ProcediaTechnology,2012,4:238-244.
[4]Bal U.Dual tree complex wavelet transform based denois-ing of optical microscopy images[J].Biomedical optics ex-press,2012,3(12):3231-3239.
[5]Singh H,Kaur L,Singh K.Fractional M-band dual tree complex wavelet transform for digital watermarking[J].Sadhana,2014,39(2):345-361.
[6]杨鑫蕊.改进的小波阈值去噪算法研究[D].哈尔滨:哈尔滨理工大学,2014.YANG Xinrui.The Study On Improve Wavelete ThresholdDenoising Method[D].Harbin:Harbin University of Sci-ence and Technology,2014.
[7]郭巍,张平,陈曦,等.基于双密度双树复数小波变换的合成孔径雷达图像降噪研究[J].电子学报,2009,37(12):2747-2752.GUO Wei,ZHANG Ping,CHEN Xi,et al.Research onSynthetic Aperture Radar Image Denoising With DoubleDensity Dual-Tree Complex Wavelet Transform[J].ActaElectronicasinica,2009,37(12):2747-2752.
[8]刘蕾.基于双树复小波变换的图像去噪[D].北京:北京化工大学,2010.LIU Lei.Image Denoising Based on The Dual Tree Com-plex Wavelet Transform[D].Beijing:Beijing University ofChemical Technology,2010.
[9]李宁,水鹏郎.一种加窗的双重局部维纳滤波图像去噪算法-基于SWT和DTCWT[J].计算机工程与应用,2007,43(28):44-46.LI Ning,SHUI Penglang.Image Desnoising Algorithm ViaDoubly Local Wiener Filtering With Windows Based OnSWT And DTCWT[J].computer Endineering and Appli-cations,2007,43(28):44-46.
[10]田园.基于多尺度小波变换的肿瘤图像纹理分析及其临床应用[D].济南:山东师范大学,2015.TIAN Yuan.The Tumor Texture Analysis And Its Clini-cal Application Based On Multi-Scale Wavelet Transform[D].Jinan:Shandong Normal University,2015.
[11]张国伟.基于小波变换的图像去噪方法研究[D].昆明:昆明理工大学,2014.ZHANG Guowei.The Research On Image DenoisingBased On Wavelet Transform[D].Kunming:KunmingUniversity of Science and Technology,2014.
[12]王芳.基于双数复小波变换的微弱生物医学信号处理及其应用研究[D].重庆:重庆大学,2014.WANG Fang.Application of Dual-Tree Complex Wave-let Transform In Weak Biomedical Signal Processing[D].Chongqing:Chongqing University,2014.
[13]Satheesh S,Prasad K.Medical image denoising usingadaptive threshold based on contourlet transform[J].ar Xiv preprint ar Xiv:1103.4907,2011.
[14]Wang Y,Fu X,Chen L,et al.DTCWT based medicalultrasound images despeckling using LS parameter opti-mization[C]//2013 IEEE International Conference on Im-age Processing.IEEE,2013:805-809.
[15]Liu Y,Jiang M.Image denoising algorithm based onDTCWT and adaptive windows[C]//Computer Scienceand Information Technology(ICCSIT),2010 3rd IEEEInternational Conference on.IEEE,2010,8:207-210.