基于视觉系统和特征提取的图像质量客观评价方法及应用研究
|
摘要
图像信息是视觉信息最重要的组成部分,是人类获取外界信息的主要途径。在数字图像的获取、处理、编码、存储、传输和重建的每一个步骤中,通常都会对图像的质量产生影响,如何评价图像质量成为图像处理、计算机视觉领域一个基本而又富有挑战的问题。本论文围绕图像失真特征的表示和提取,从多特征融合、多尺度几何分析以及人眼视觉系统建模等方面研究图像质量的客观评价问题,并将常规图像质量分析和度量方法应用在医学图像领域。主要工作和创新成果如下:
1.对图像视觉质量失真的表示和失真特征提取进行了分析和比较,通过数学推导讨论了图像失真特征的不同表示方法以及他们之间的内在关联。在此基础上,引入典型相关分析方法,将多种失真特征有效融合在一起,给出基于多特征的图像质量评价算法,优化了特征选择方式,并使其可以根据失真类型给出更为准确、稳健的图像质量评价结果,能全面地综合评价图像质量。
2.图像结构特征的表示和度量是图像质量评价的关键技术之一,本文引入多尺度几何分析方法来提取图像的结构特征、方向特征和多分辨率特征,逼近人眼视觉系统对图像信号的多通道分解过程。在此基础上,提出一种多尺度方向性差异模型,表示和度量图像质量的视觉失真程度。
3.在充分考虑前端人眼视觉系统特性的基础上,本文引入视觉感知在频率和方向上的多通道分解特性、对比度敏感特性、视觉掩蔽效应、频带响应和误差融合模型,与图像的结构特征表示方法有效结合,提高了基于特征提取的图像质量评价方法的准确性,同时改进了基于单纯人眼视觉系统特性模型算法的复杂性,取得了较好的效果。
4.在对图像信号的多通道分解过程中,基于视觉系统对不同频率的响应特性,提出了对频带误差的线性融合方法。在综合性图像数据库上与非线性频带误差融合方法相比较,证实了线性误差融合方法能够满足对视觉系统的逼近要求,节省了计算成本的同时提高了图像质量预测的准确度。
5.针对临床CT成像应用中保证图像质量可辨度的条件下降低射线剂量的要求,本文研究了客观图像质量评价算法在计算机辅助评价CT图像质量中的应用,特别是对低剂量CT图像的质量评价问题。针对CT图像质量失真,如噪声增加、空间分辨率降低、对比度降低等,本文在经典的图像信噪比评价方法的基础上,增加视觉信息保真测度、结构相似性测度的应用。改变CT射线剂量,以医生判断作为标准,评估客观图像质量评价测度于CT图像的性能,进而分析CT图像的视觉失真特征。在对测试体膜和动物切片进行的大量实验中,复小波域的结构相似性测度给出了与医生专业评判相符的结果。
Among the visual information received by human visual system images arethe matters of primary importance. In the procedures of image processing sys-tem, e.g., acquisition, processing, coding, storage, transmission and reproduction,digital image may be in degradation in visual quality, so evaluating image qual-ity becomes a fundamental job in the field of image processing and computervision. This thesis investigates objective image quality assessment problem fromthe viewpoint of decision fusion, structural feature extraction and multi-scale ge-ometric analysis. Furthermore, the application of general objective image qualityapproach in medical image evaluation is studied and performed. The main workand innovations are listed as follows:
1. The research background is extensively reviewed, and some popular im-age quality measures are analyzed and compared from the aspect of distortiondescription and feature extraction. Moreover, the ideal of canonical correlationanalysis is introduced, which keeps the e?ective discriminating information ofmulti-feature as well as eliminates the redundant information inside them tocertain degree. A Supervised Multi-Feature (SMF) full-reference image qualityevaluation algorithm is proposed. Compared with the state-of-the-art image qual-ity metrics, the proposed method improves accuracy and robustness of qualityprediction.
2. Propose a novel objective full-reference image quality assessment metricbased on multiscale geometric analysis. The multichannel behavior of the humanvision system is emulated by contourlet transform, a perceptual subband decom-position. The distortion feature and level is emulated by a multi-scale directionaldi?erence model.
3. The HVS model of the low-level perception used in this metric includessubband decomposition, contrast masking, entropy masking, and error pool-ing. Extensive validation experiments are performed on two professional imagedatabases.The proposed method displayed a higher prediction accuracy and ro-bustness across extensive distortion types and a broad range of distortion level,exhibiting a generally better performance.
4 .In the error pooling stage, a linear fusion scheme for subband distortionis proposed to trade the frequency properties of the HVS and computation cost.The nonlinear fusion scheme of the Minkowski summation is also implemented forcomparison. Finally, the advantages and limitations of these are compared anddiscussed. Owing to the the employment of the frequency properties of HVS andthe Weber-Fechner law, the linear fusion scheme of subband distortion proved tobe a preferable alternative for the Minkwoski summation.
5. Computed tomography (CT) is an essential imaging modality. To solvethe problem of increasing radiation exposure from CT scanner and image qual-ity, general objective image quality methods are applied in CT image qualityevaluation. After analyzing perceptual feature of CT image, several popular ob-jective image quality metrics, which focus on the similar perceptual features, aretested on the CT image of phantom and animals. A lot of experiments are per-formed. Compared with the subjective ratings from two professional radiationphysicians, the complex wavelet-based structural similarity metric presents thebest CT image quality prediction results.
1.对图像视觉质量失真的表示和失真特征提取进行了分析和比较,通过数学推导讨论了图像失真特征的不同表示方法以及他们之间的内在关联。在此基础上,引入典型相关分析方法,将多种失真特征有效融合在一起,给出基于多特征的图像质量评价算法,优化了特征选择方式,并使其可以根据失真类型给出更为准确、稳健的图像质量评价结果,能全面地综合评价图像质量。
2.图像结构特征的表示和度量是图像质量评价的关键技术之一,本文引入多尺度几何分析方法来提取图像的结构特征、方向特征和多分辨率特征,逼近人眼视觉系统对图像信号的多通道分解过程。在此基础上,提出一种多尺度方向性差异模型,表示和度量图像质量的视觉失真程度。
3.在充分考虑前端人眼视觉系统特性的基础上,本文引入视觉感知在频率和方向上的多通道分解特性、对比度敏感特性、视觉掩蔽效应、频带响应和误差融合模型,与图像的结构特征表示方法有效结合,提高了基于特征提取的图像质量评价方法的准确性,同时改进了基于单纯人眼视觉系统特性模型算法的复杂性,取得了较好的效果。
4.在对图像信号的多通道分解过程中,基于视觉系统对不同频率的响应特性,提出了对频带误差的线性融合方法。在综合性图像数据库上与非线性频带误差融合方法相比较,证实了线性误差融合方法能够满足对视觉系统的逼近要求,节省了计算成本的同时提高了图像质量预测的准确度。
5.针对临床CT成像应用中保证图像质量可辨度的条件下降低射线剂量的要求,本文研究了客观图像质量评价算法在计算机辅助评价CT图像质量中的应用,特别是对低剂量CT图像的质量评价问题。针对CT图像质量失真,如噪声增加、空间分辨率降低、对比度降低等,本文在经典的图像信噪比评价方法的基础上,增加视觉信息保真测度、结构相似性测度的应用。改变CT射线剂量,以医生判断作为标准,评估客观图像质量评价测度于CT图像的性能,进而分析CT图像的视觉失真特征。在对测试体膜和动物切片进行的大量实验中,复小波域的结构相似性测度给出了与医生专业评判相符的结果。
Among the visual information received by human visual system images arethe matters of primary importance. In the procedures of image processing sys-tem, e.g., acquisition, processing, coding, storage, transmission and reproduction,digital image may be in degradation in visual quality, so evaluating image qual-ity becomes a fundamental job in the field of image processing and computervision. This thesis investigates objective image quality assessment problem fromthe viewpoint of decision fusion, structural feature extraction and multi-scale ge-ometric analysis. Furthermore, the application of general objective image qualityapproach in medical image evaluation is studied and performed. The main workand innovations are listed as follows:
1. The research background is extensively reviewed, and some popular im-age quality measures are analyzed and compared from the aspect of distortiondescription and feature extraction. Moreover, the ideal of canonical correlationanalysis is introduced, which keeps the e?ective discriminating information ofmulti-feature as well as eliminates the redundant information inside them tocertain degree. A Supervised Multi-Feature (SMF) full-reference image qualityevaluation algorithm is proposed. Compared with the state-of-the-art image qual-ity metrics, the proposed method improves accuracy and robustness of qualityprediction.
2. Propose a novel objective full-reference image quality assessment metricbased on multiscale geometric analysis. The multichannel behavior of the humanvision system is emulated by contourlet transform, a perceptual subband decom-position. The distortion feature and level is emulated by a multi-scale directionaldi?erence model.
3. The HVS model of the low-level perception used in this metric includessubband decomposition, contrast masking, entropy masking, and error pool-ing. Extensive validation experiments are performed on two professional imagedatabases.The proposed method displayed a higher prediction accuracy and ro-bustness across extensive distortion types and a broad range of distortion level,exhibiting a generally better performance.
4 .In the error pooling stage, a linear fusion scheme for subband distortionis proposed to trade the frequency properties of the HVS and computation cost.The nonlinear fusion scheme of the Minkowski summation is also implemented forcomparison. Finally, the advantages and limitations of these are compared anddiscussed. Owing to the the employment of the frequency properties of HVS andthe Weber-Fechner law, the linear fusion scheme of subband distortion proved tobe a preferable alternative for the Minkwoski summation.
5. Computed tomography (CT) is an essential imaging modality. To solvethe problem of increasing radiation exposure from CT scanner and image qual-ity, general objective image quality methods are applied in CT image qualityevaluation. After analyzing perceptual feature of CT image, several popular ob-jective image quality metrics, which focus on the similar perceptual features, aretested on the CT image of phantom and animals. A lot of experiments are per-formed. Compared with the subjective ratings from two professional radiationphysicians, the complex wavelet-based structural similarity metric presents thebest CT image quality prediction results.
引文
[1] Bovik A.C., Handbook of image and video processing (communications, net-working and multimedia), Academic Press, Inc. Orlando, FL, USA, 2005.
[2] Sheikh H., Image quality assessment using natural scene statistics, Ph.D.thesis, 2004.
[3] Wang Z., Bovik A., and Lu L.,“Why is image quality assessment so dif-ficult?”, IEEE International Conference on Acoustics Speech and SignalProcessing, volume 4, Citeseer, 2002, 3313–3316.
[4] Wang Z. and Bovik A.,“A universal image quality index”, IEEE SignalProcessing Letters, 2002, 9(3), 81–84.
[5] Sheikh H.R., Sabir M.F., and Bovik A.C.,“A statistical evaluation of recentfull reference image quality assessment algorithms”, IEEE Transactions onimage processing, 2006, 15(11), 3440–3451.
[6] Pedersen M., Hardeberg J., and Nussbaum P.,“Using gaze informationto improve image di?erence metrics”, Proceedings of SPIE, volume 6806,2008, 680611.
[7] Teo P.C. and Heeger D.J.,“Perceptual image distortion”, Sid internationalsymposium digest of technical papers, volume 25, society for informationdisplay, 1994, 209–209.
[8] Zhou Y., Chen D., Li C., et al.,“A practice of medical image qualityevaluation”, Neural Networks and Signal Processing, 2003. Proceedings ofthe 2003 International Conference on, volume 1, 2003.
[9] Wang Z., Bovik A., Sheikh H., et al.,“Image quality assessment: From errorvisibility to structural similarity”, IEEE transactions on image processing,2004, 13(4), 600–612.
[10] Sheikh H., Bovik A., and De Veciana G.,“An information fidelity criterionfor image quality assessment using natural scene statistics”, IEEE Trans-actions on image processing, 2005, 14(12), 2117–2128.
[11] Sheikh H. and Bovik A.,“Image information and visual quality”, IEEETransactions on Image Processing, 2006, 15(2), 430–444.
[12] Wainwright M., Simoncelli E., and Willsky A.,“Random cascades onwavelet trees and their use in analyzing and modeling natural images”,Applied and Computational Harmonic Analysis, 2001, 11(1), 89–123.
[13] Srivastava A., Lee A., Simoncelli E., et al.,“On advances in statisticalmodeling of natural images”, Journal of mathematical imaging and vision,2003, 18(1), 17–33.
[14] Li Q. and Wang Z.,“General-purpose reduced-reference image quality as-sessment based on perceptually and statistically motivated image represen-tation”, 15th IEEE International Conference on Image Processing, 2008.ICIP 2008, 2008, 1192–1195.
[15] Wang Z. and Simoncelli E.,“An adaptive linear system framework forimage distortion analysis”, Proc. of ICIP, volume 3, Citeseer, 2005, 1160–1163.
[16] Channappayya S., Bovik A., and Heath Jr R.,“Rate Bounds on SSIMIndex of Quantized Images”, IEEE Transactions on Image Processing, 2008,17(9), 1624–1639.
[17]杨春玲,陈冠豪, and谢胜利,“基于梯度信息的图像质量评判方法的研究”,电子学报, 2007, 35(007), 1313–1317.
[18]袁飞,黄联芬, and姚彦,“基于视觉掩盖效应和奇异值分解的图像质量评测方法”,光学精密工程, 2008, 16(004), 706–713.
[19]汪孔桥,“数字图像的质量评价”,测控技术, 2000, 19, 14–16.
[20] Eskicioglu A. and Fisher P.,“Image quality measures and their perfor-mance”, IEEE Transactions on Communications, 1995, 43(12), 2959–2965.
[21] Watson A.B., Borthwick R., and Taylor M.,“Image quality and entropymasking”, Proc. SPIE, volume 3016, 1997, 2–12.
[22] Ninassi A., Le Meur O., Le Callet P., et al.,“On the performance of hu-man visual system based image quality assessment metric using waveletdomain”, Proc. SPIE Human Vision and Electronic Imaging XIII, volume6806, 2008.
[23] Wang Z. and Bovik A.,“Modern image quality assessment”, SynthesisLectures on Image, Video, and Multimedia Processing, 2006, 2(1), 1–156.
[24] WATSON A.,“The cortex transform- Rapid computation of simulatedneural images”, Computer Vision, Graphics, and Image Processing, 1987,39(3), 311–327.
[25] Daly S.,“The visible di?erences predictor: An algorithm for the assessmentof image fidelity”, Digital images and human vision, 1993, 11.
[26] Lubin J.,“The use of psychophysical data and models in the analysis of dis-play system performance”, Digital Images and Human Vision, MIT PressCambridge, MA, USA, 1993, 163–178.
[27] Simoncelli E., Freeman W., Adelson E., et al.,“Shiftable multiscale trans-forms”, IEEE Transactions on Information Theory, 1992, 38(2), 587–607.
[28] Chou C. and Li Y.,“A perceptually tuned subband image coder basedon the measure ofjust-noticeable-distortion profile”, IEEE transactions oncircuits and systems for video technology, 1995, 5(6), 467–476.
[29] Watson A.B.,“DCT quantization matrices visually optimized for individualimages”, proc. SPIE, volume 1913, 1993, 202–216.
[30] Watson A., Yang G., Solomon J., et al.,“Visibility of wavelet quantizationnoise”, IEEE Transactions on Image Processing, 1997, 6(8), 1164–1175.
[31] Simoncelli E.P. and Olshausen B.A.,“Natural image statistics and neuralrepresentation”, Annu. Rev. Neurosci., 2001, 24, 1193–1216.
[32] Buccigrossi R. and Simoncelli E.,“Image compression via joint statisti-cal characterization in the wavelet domain”, IEEE Transactions on ImageProcessing, 1999, 8(12), 1688–1701.
[33] Wang Z., Simoncelli E., and Bovik A.,“Multi-scale structural similarityfor image quality assessment”, Asilomar conference on signals systems andcomputers, volume 2, IEEE; 1998, 2003, 1398–1402.
[34] Wang Z. and Simoncelli E.,“Translation insensitive image similarity incomplex wavelet domain”, IEEE International Conference on Acoustics,Speech, and Signal Processing, 2005. Proceedings.(ICASSP’05), volume 2,2005.
[35] Yang C.L., Gao W.R., and Po L.M.,“Discrete wavelet transform-basedstructural similarity for image quality assessment”, 15th IEEE Interna-tional Conference on Image Processing, 2008. ICIP 2008, 2008, 377–380.
[36]杨春玲,旷开智,陈冠豪, et al.,“基于梯度的结构相似度的图像质量评价方法”,华南理工大学学报:自然科学版, 2006, 34(009), 22–25.
[37] Gao X., Lu W., Li X., et al.,“Wavelet-based contourlet in quality evalua-tion of digital images”, Neurocomputing, 2008, 72(1-3), 378–385.
[38]朱大龙and明军,“基于结构失真的图像质量评价方法的研究”,计算机技术与发展, 2006, 16(003), 56–57.
[39] Andre T., Antonini M., Barlaud M., et al.,“Entropy-based distortion mea-sure and bit allocation for wavelet image compression”, IEEE Transactionson Image Processing, 2007, 16(12), 3058–3064.
[40] Ginesu G., Massidda F., and Giusto D.,“A multi-factors approach forimage quality assessment based on a human visual system model”, SignalProcessing: Image Communication, 2006, 21(4), 316–333.
[41] Delgorge C., Rosenberger C., Poisson G., et al.,“Towards a new tool for theevaluation of the quality of ultrasound compressed images”, IEEE transac-tions on medical imaging, 2006, 25(11), 1502–1509.
[42]韦学辉,李均利,陈刚,“基于多元线性回归的图像质量评价方法”,中国图象图形学报, 2008, 13(11), 2123–2131.
[43] Itti L. and Koch C.,“Computational modelling of visual attention”, NatureReviews Neuroscience, 2001, 2(3), 194–203.
[44] Oliva A., Torralba A., Castelhano M., et al.,“Top-down control of visualattention in object detection”, Image Processing, 2003. ICIP 2003. Pro-ceedings. 2003 International Conference on, volume 1, 2003.
[45] Kadiyala V., Pinneli S., Larson E., et al.,“Quantifying the perceived in-terest of objects in images: e?ects of size, location, blur, and contrast”,Proceedings of SPIE, volume 6806, 2008, 68060S.
[46] Slowe T. and Marsic I.,“Saliency-based visual representation for compres-sion”, Image Processing, 1997. Proceedings., International Conference on,volume 2, 1997.
[47] Menegaz G. and Zambon R.,“Towards a semantic-driven metric for imagequality”, IEEE International Conference on Image Processing, 2005. ICIP2005, volume 3, 2005.
[48] Ninassi A., Le Meur O., Le Callet P., et al.,“Does where you Gaze on anImage A?ect your Perception of Quality? Applying Visual Attention to Im-age Quality Metric”, IEEE International Conference on Image Processing,2007. ICIP 2007, volume 2, 2007.
[49] Larson E., Vu C., and Chandler D.,“Can visual fixation patterns improveimage fidelity assessment?”, 15th IEEE International Conference on ImageProcessing, 2008. ICIP 2008, 2008, 2572–2575.
[50] Sadaka N., Karam L., Ferzli R., et al.,“A no-reference perceptual imagesharpness metric based on saliency-weighted foveal pooling”, 15th IEEE In-ternational Conference on Image Processing, 2008. ICIP 2008, 2008, 369–372.
[51] Lu Z., Lin W., Ong E., et al.,“Perceptual-quality significance map (PQSM)and its application on video quality distortion metrics”, IEEE Interna-tional conference on acoustics speech and signal processing, volume 3, 2003,III:617–620.
[52] Barland R. and Saadane A.,“Blind Quality Metric using a PerceptualImportance Map for JPEG-20000 Compressed Images”, 2006 IEEE Inter-national Conference on Image Processing, 2006, 2941–2944.
[53]马文波,赵保军,任宏亮, et al.,“基于小波频带划分及CSF特性的图像质量评价方法”,激光与红外, 2007, 37(007), 687–690.
[54] Zhang M. and Mou X.,“A psychovisual image Quality Metric based onmulti-scale Structure Similarity”, 15th IEEE International Conference onImage Processing, 2008. ICIP 2008, 2008, 381–384.
[55] Ninassi A., Le Meur O., Le Callet P., et al.,“Which Semi-Local VisualMasking Model For Wavelet Based Image Quality Metric?”, Proc. IEEEInternational Conference on Image Processing (to be appeared in), 2008.
[56] Channappayya S., Bovik A., Caramanis C., et al.,“Design of linear equal-izers optimized for the structural similarity index”, IEEE Transactions onImage Processing, 2008, 17(6), 857–872.
[57] Brooks A., Zhao X., and Pappas T.,“Structural similarity quality metricsin a coding context: exploring the space of realistic distortions”, IEEETransactions on Image Processing, 2008, 17(8), 1261–1273.
[58] Ghanem B., Resendiz E., and Ahuja N.,“Segmentation-based PerceptualImage Quality Assessment (SPIQA)”, 15th IEEE International Conferenceon Image Processing, 2008. ICIP 2008, 2008, 393–396.
[59] Dixon T., Canga E., Nikolov S., et al.,“Selection of image fusion qualitymeasures: objective, subjective, and metric assessment”, Journal of theOptical Society of America A, 2007, 24(12), 125–135.
[60] Grasruck M., Suess C., Stierstorfer K., et al.,“Evaluation of image qualityand dose on a ?at-panel CT-scanner”, Proceedings of SPIE, volume 5745,2005, 179.
[61] Vite C., Mangini M., Strocchi S., et al.,“Dosimetric and image qualityassessment of di?erent acquisition protocols of a novel 64-slice ct scanner”,Proceedings of SPIE, volume 6142, 2006, 61423G.
[62] Londt J., Shreter U., Vass M., et al.,“Dose reduction of up to 89% whilemaintaining image quality in cardiovascular ct achieved with prospectiveecg gating”, Proceedings of SPIE, volume 6510, 2007, 65102S.
[63] McGarry C., Grattan M., and Cosgrove V.,“Optimization of image qualityand dose for Varian aS500 electronic portal imaging devices (EPIDs)”,Physics in Medicine and Biology, 2007, 52(23), 6865–6878.
[64] Watson A., Hu J., and McGowan III J.,“Digital video quality metric basedon human vision”, Journal of Electronic Imaging, 2001, 10, 20.
[65] Wang Z., Lu L., and Bovik A.,“Video quality assessment based on struc-tural distortion measurement”, Signal processing: Image communication,2004, 19(2), 121–132.
[66] Ndjiki-Nya P., Barrado M., and Wiegand T.,“E?cient Full-Reference As-sessment of Image and Video Quality”, IEEE International Conference onImage Processing, 2007. ICIP 2007, volume 2, 2007.
[67] Fullerton M., Woods R., Vera-Diaz F., et al.,“Measuring perceived videoquality of MPEG enhancement by people with impaired vision”, Journalof the Optical Society of America A, 2007, 24(12), 174–187.
[68] Varadarajan S. and Karam L.,“An improved perception-based no-referenceobjective image sharpness metric using iterative edge refinement”, 15thIEEE International Conference on Image Processing, 2008. ICIP 2008,2008, 401–404.
[69] Seshadrinathan K. and Bovik A.,“Unifying analysis of full reference imagequality assessment”, 15th IEEE International Conference on Image Pro-cessing, 2008. ICIP 2008, 2008, 1200–1203.
[70] Reibman A. and Poole D.,“Characterizing packet-loss impairments in com-pressed video”, IEEE International Conference on Image Processing, 2007.ICIP 2007, volume 5, 2007.
[71] Gel’Fand I. and Yaglom A.,“Calculation of the amount of informationabout a random function contained in another such function”, Amer. Math.Soc. Translat., II Ser, 1959, 12, 199–246.
[72] Wainwright M. and Simoncelli E.,“Scale mixtures of Gaussians and thestatistics of natural images”, Advances in neural information processingsystems, 2000, 12(1), 855–861.
[73] Sheikh H.R., Wang Z., Bovik A.C., et al.,“Image and video quality assess-ment research at LIVE”, .
[74] Chandler D.M. and Hemami S.S.,“VSNR: A wavelet-based visual signal-to-noise ratio for natural images”, IEEE Transactions on Image Processing,2007, 16(9), 2284–2298.
[75] Gaubatz M. and Hemami S.,“On the nearly scale-independent rank be-havior of image quality metrics”, 15th IEEE International Conference onImage Processing, 2008. ICIP 2008, 2008, 701–704.
[76] Rohaly A.M., Libert J., Corriveau P., et al.,“Final report from the videoquality experts group on the validation of objective models of video qualityassessment”, 2003.
[77] Borga M., of Electrical Engineering D., and i Link¨oping U., Learningmultidimensional signal processing, Department of Electrical Engineering,Link¨oping University, 1998.
[78] Melzer T., Reiter M., and Bischof H.,“Appearance models based on kernelcanonical correlation analysis”, Pattern recognition, 2003, 36(9), 1961–1971.
[79] Yu S.,“Direct blind channel equalization via the programmable canonicalcorrelation analysis”, Signal Processing, 2001, 81(8), 1715–1724.
[80] Fidler S., Skocaj D., and Leonardis A.,“Combining reconstructive anddiscriminative subspace methods for robust classification and regression bysubsampling”, IEEE transactions on pattern analysis and machine intelli-gence, 2006, 28(3), 337–350.
[81]孙权森,曾生根,王平安, et al.,“典型相关分析的理论及其在特征融合中的应用”,计算机学报, 2005, 28(009), 1524–1533.
[82] Hotelling H.,“RELATIONS BETWEEN TWO SETS OF VARIATES*”,1936.
[83] Sun Q., Zeng S., Liu Y., et al.,“A new method of feature fusion andits application in image recognition”, Pattern Recognition, 2005, 38(12),2437–2448.
[84] Sampat M., Wang Z., Markey M., et al.,“Measuring intra-and inter-observer agreement in identifying and localizing structures in medical im-ages”, 2006 IEEE International Conference on Image Processing, 2006,81–84.
[85] Chen G., Yang C., and Xie S.,“Gradient-based structural similarity forimage quality assessment”, 2006 IEEE International Conference on ImageProcessing, 2006, 2929–2932.
[86] Wang Z. and Simoncelli E.,“Reduced-reference image quality assessmentusing a wavelet-domain natural image statistic model”, Proc. of SPIE Hu-man Vision and Electronic Imaging, volume 5666, 2005, 149–159.
[87] Candes E., Ridgelets: theory and applications, Stanford University, 1998.
[88] Candes E.,“Monoscale ridgelets for the representation of images withedges”, Dept. Statist., Stanford Univ., Stanford, CA, Tech. Rep, 1999.
[89]焦李成and谭山,“图像的多尺度几何分析:回顾和展望”,电子学报, 2004,31(B12), 1975–1981.
[90] Donoho D.,“Sparse components analysis and optimal atomic decomposi-tion”, Constructive Approximation, 2001, 17, 353–382.
[91] Do M. and Vetterli M.,“Contourlets: a new directional multiresolutionimage representation”, Signals, Systems and Computers, 2002. ConferenceRecord of the Thirty-Sixth Asilomar Conference on, volume 1, 2002.
[92] da Cunha A., Zhou J., and Do M.,“Nonsubsampled contourlet transform:filter design and applications in Denoising”, IEEE International Conferenceon Image Processing, Genoa, Italy, volume 11, 2005, 14.
[93] Zhou J., Cunha A., and Do M.,“Nonsubsampled contourlet transform:construction and application in enhancement”, Proceeding of IEEE inter-national conference on Image processing, volume 1, 2005, 469–472.
[94] Eslami R. and Radha H.,“On low bit-rate coding using the contourlettransform”, ASILOMAR CONFERENCE ON SIGNALS SYSTEMS ANDCOMPUTERS, volume 2, Citeseer, 2003, 1524–1528.
[95] Eslami R. and Radha H.,“Wavelet-based contourlet transform and its ap-plication to image coding”, proc. of IEEE International Conference on Im-age Processing, 2004.
[96] Candes E. and Donoho D.,“Curvelets―a surprisingly e?ective nonadaptiverepresentation for objects with edges. Curves and Surfaces, 105–120. Editedby C. Rabut, A. Cohen, and LL Schumaker”, 2000.
[97] Burt P. and II E.,“Adelson,”The Laplacian pyramid as a compact imagecode,””, IEEE Trans. Commun, 1983, 31(4), 532–540.
[98] Do M.N., Directional multi-resolution Image Representation, Ph.D. thesis,Department of Communication Systems, Swiss Federal Institute of Tech-nology Lausanne, 2001.
[99]庞建新,图像质量客观评价的研究, Ph.D. thesis,中国科技大学, 2008.
[100] Lu W., Gao X., Li X., et al.,“An image quality assessment metric basedcontourlet”, 15th IEEE International Conference on Image Processing,2008. ICIP 2008, 2008, 1172–1175.
[101] Levine M.W. and Shefner J.M., Fundamentals of sensation and perception,Brooks/Cole Pub Co, 1991.
[102] Van den Branden Lambrecht C.J. and Verscheure O.,“Perceptual qual-ity measure using a spatio-temporal model of the human visual system”,Proceedings-SPIE the international society for optical engineering, Interna-tional Society for Optical Engineering, 1996, 450–461.
[103] Eisenberg R. (ed.), Radiology: An illustrated history., chapter Injury andProtection., 1992, 156–182.
[104] Brasch R., Boyd D., and Gooding C.,“Computed tomographic scanning inchildren: comparison of radiation dose and resolving power of commercialCT scanners”, American Journal of Roentgenology, 1978, 131(1), 95.
[105] Brenner D., Elliston C., Hall E., et al.,“Estimated risks of radiation-induced fatal cancer from pediatric CT”, American Journal of Roentgenol-ogy, 2001, 176(2), 289.
[106] Liu X., Primak A., Yu L., et al.,“Quantitative evaluation of noise reductionalgorithms for very low dose renal CT perfusion imaging”, Proceedings ofSPIE, volume 7258, 2009, 72581T.
[107] Rivetti S., Lanconelli N., Bertolini M., et al.,“Performance evaluation of adirect computed radiography system by means of physical characterizationand contrast detail analysis”, Proceedings of SPIE, volume 6510, 2007,65104M.
[108] Fan J., Dong F., Sainath P., et al.,“Image quality evaluation of a lightspeedCT750 HD computed tomography system”, Proceedings of SPIE, volume7258, 2009, 72584S.
[109] Vite C., Mangini M., Strocchi S., et al.,“Dosimetric and image qualityassessment of di?erent acquisition protocols of a novel 64-slice CT scanner”,Proceedings of SPIE, volume 6142, 2006, 61423G.
[110] Londt J., Shreter U., Vass M., et al.,“Dose reduction of up to 89% whilemaintaining image quality in cardiovascular CT achieved with prospectiveECG gating”, Proceedings of SPIE, volume 6510, 2007, 65102S.
[111] Strocchi S., Vite C., Novario R., et al.,“Computed tomography qualityindexes: evaluation experience”, Proceedings of SPIE, volume 7258, 2009,72584T.
[112] Leong D. and Brennan P.,“Optimizing image quality using statistical mul-tivariate optimization methodology using desirability functions”, Proceed-ings of SPIE, volume 7258, 2009, 72584U.
[113] Schopphoven S., Faulkner K., and Busch H.,“Assessment of patient or-gan dose in CT virtual colonoscopy for bowel cancer screening”, RadiationProtection Dosimetry, 2008.
[114] Li X., Samei E., Segars W., et al.,“Patient-specific dose estimation forpediatric abdomen-pelvis CT”, Proceedings of SPIE, volume 7258, 2009,725804.
[115] Brisse H., Brenot J., Pierrat N., et al.,“The relevance of image quality in-dices for dose optimization in abdominal multi-detector row CT in children:experimental assessment with pediatric phantoms”, Physics in Medicineand Biology, 2009, 54, 1871–1892.
[116] Lee S., Wang J., Liu S., et al.,“Evaluation of dose–image-quality optimiza-tion in digital chest radiography”, Nuclear Inst. and Methods in PhysicsResearch, A, 2007, 580(1), 544–547.
[117] Yamamoto S., Horiuchi T., Sekiguchi J., et al.,“Design of a DICOM image-based program for estimating patient exposure dose in computed tomogra-phy”, Technology and Health Care, 2007, 15(2), 147–156.
[118] Kazama M., Tsukagoshi S., and Okumura M.,“Image quality improvementand exposure dose reduction with the combined use of X-ray modulationand Boost3D”, Proceedings of SPIE, volume 6142, 2006, 61422G.
[119] Donnelly L., Emery K., Brody A., et al.,“Minimizing radiation dose forpediatric body applications of single-detector helical CT: strategies at alarge Children’s Hospital.”, AJR. American journal of roentgenology, 2001,176(2), 303.
[120]王学礼,“CT图像质量评价技术中若干问题的探讨”, CT理论与应用研究, 2003, 12(003), 46–52.
[121]王学民,沈克涵编著,医学成像系统,清华大学出版社, 2006.
[122] Cosman P., Gray R., and Olshen R.,“Evaluating quality of compressedmedical images: SNR, subjectiverating, and diagnostic accuracy”, volume82, 1994, 919–932.
[123] Cosman P., Tseng C., Gray R., et al.,“Tree-structured vector quantizationof CT chest scans: image quality and diagnostic accuracy”, IEEE transac-tions on medical imaging, 1993, 12(4), 727–739.
[124] Young K., Alsager A., Dance D., et al.,“Predicting contrast detail perfor-mance from objective measurements in digital mammography”, Proceedingsof SPIE, volume 7258, 2009, 72581C.
[2] Sheikh H., Image quality assessment using natural scene statistics, Ph.D.thesis, 2004.
[3] Wang Z., Bovik A., and Lu L.,“Why is image quality assessment so dif-ficult?”, IEEE International Conference on Acoustics Speech and SignalProcessing, volume 4, Citeseer, 2002, 3313–3316.
[4] Wang Z. and Bovik A.,“A universal image quality index”, IEEE SignalProcessing Letters, 2002, 9(3), 81–84.
[5] Sheikh H.R., Sabir M.F., and Bovik A.C.,“A statistical evaluation of recentfull reference image quality assessment algorithms”, IEEE Transactions onimage processing, 2006, 15(11), 3440–3451.
[6] Pedersen M., Hardeberg J., and Nussbaum P.,“Using gaze informationto improve image di?erence metrics”, Proceedings of SPIE, volume 6806,2008, 680611.
[7] Teo P.C. and Heeger D.J.,“Perceptual image distortion”, Sid internationalsymposium digest of technical papers, volume 25, society for informationdisplay, 1994, 209–209.
[8] Zhou Y., Chen D., Li C., et al.,“A practice of medical image qualityevaluation”, Neural Networks and Signal Processing, 2003. Proceedings ofthe 2003 International Conference on, volume 1, 2003.
[9] Wang Z., Bovik A., Sheikh H., et al.,“Image quality assessment: From errorvisibility to structural similarity”, IEEE transactions on image processing,2004, 13(4), 600–612.
[10] Sheikh H., Bovik A., and De Veciana G.,“An information fidelity criterionfor image quality assessment using natural scene statistics”, IEEE Trans-actions on image processing, 2005, 14(12), 2117–2128.
[11] Sheikh H. and Bovik A.,“Image information and visual quality”, IEEETransactions on Image Processing, 2006, 15(2), 430–444.
[12] Wainwright M., Simoncelli E., and Willsky A.,“Random cascades onwavelet trees and their use in analyzing and modeling natural images”,Applied and Computational Harmonic Analysis, 2001, 11(1), 89–123.
[13] Srivastava A., Lee A., Simoncelli E., et al.,“On advances in statisticalmodeling of natural images”, Journal of mathematical imaging and vision,2003, 18(1), 17–33.
[14] Li Q. and Wang Z.,“General-purpose reduced-reference image quality as-sessment based on perceptually and statistically motivated image represen-tation”, 15th IEEE International Conference on Image Processing, 2008.ICIP 2008, 2008, 1192–1195.
[15] Wang Z. and Simoncelli E.,“An adaptive linear system framework forimage distortion analysis”, Proc. of ICIP, volume 3, Citeseer, 2005, 1160–1163.
[16] Channappayya S., Bovik A., and Heath Jr R.,“Rate Bounds on SSIMIndex of Quantized Images”, IEEE Transactions on Image Processing, 2008,17(9), 1624–1639.
[17]杨春玲,陈冠豪, and谢胜利,“基于梯度信息的图像质量评判方法的研究”,电子学报, 2007, 35(007), 1313–1317.
[18]袁飞,黄联芬, and姚彦,“基于视觉掩盖效应和奇异值分解的图像质量评测方法”,光学精密工程, 2008, 16(004), 706–713.
[19]汪孔桥,“数字图像的质量评价”,测控技术, 2000, 19, 14–16.
[20] Eskicioglu A. and Fisher P.,“Image quality measures and their perfor-mance”, IEEE Transactions on Communications, 1995, 43(12), 2959–2965.
[21] Watson A.B., Borthwick R., and Taylor M.,“Image quality and entropymasking”, Proc. SPIE, volume 3016, 1997, 2–12.
[22] Ninassi A., Le Meur O., Le Callet P., et al.,“On the performance of hu-man visual system based image quality assessment metric using waveletdomain”, Proc. SPIE Human Vision and Electronic Imaging XIII, volume6806, 2008.
[23] Wang Z. and Bovik A.,“Modern image quality assessment”, SynthesisLectures on Image, Video, and Multimedia Processing, 2006, 2(1), 1–156.
[24] WATSON A.,“The cortex transform- Rapid computation of simulatedneural images”, Computer Vision, Graphics, and Image Processing, 1987,39(3), 311–327.
[25] Daly S.,“The visible di?erences predictor: An algorithm for the assessmentof image fidelity”, Digital images and human vision, 1993, 11.
[26] Lubin J.,“The use of psychophysical data and models in the analysis of dis-play system performance”, Digital Images and Human Vision, MIT PressCambridge, MA, USA, 1993, 163–178.
[27] Simoncelli E., Freeman W., Adelson E., et al.,“Shiftable multiscale trans-forms”, IEEE Transactions on Information Theory, 1992, 38(2), 587–607.
[28] Chou C. and Li Y.,“A perceptually tuned subband image coder basedon the measure ofjust-noticeable-distortion profile”, IEEE transactions oncircuits and systems for video technology, 1995, 5(6), 467–476.
[29] Watson A.B.,“DCT quantization matrices visually optimized for individualimages”, proc. SPIE, volume 1913, 1993, 202–216.
[30] Watson A., Yang G., Solomon J., et al.,“Visibility of wavelet quantizationnoise”, IEEE Transactions on Image Processing, 1997, 6(8), 1164–1175.
[31] Simoncelli E.P. and Olshausen B.A.,“Natural image statistics and neuralrepresentation”, Annu. Rev. Neurosci., 2001, 24, 1193–1216.
[32] Buccigrossi R. and Simoncelli E.,“Image compression via joint statisti-cal characterization in the wavelet domain”, IEEE Transactions on ImageProcessing, 1999, 8(12), 1688–1701.
[33] Wang Z., Simoncelli E., and Bovik A.,“Multi-scale structural similarityfor image quality assessment”, Asilomar conference on signals systems andcomputers, volume 2, IEEE; 1998, 2003, 1398–1402.
[34] Wang Z. and Simoncelli E.,“Translation insensitive image similarity incomplex wavelet domain”, IEEE International Conference on Acoustics,Speech, and Signal Processing, 2005. Proceedings.(ICASSP’05), volume 2,2005.
[35] Yang C.L., Gao W.R., and Po L.M.,“Discrete wavelet transform-basedstructural similarity for image quality assessment”, 15th IEEE Interna-tional Conference on Image Processing, 2008. ICIP 2008, 2008, 377–380.
[36]杨春玲,旷开智,陈冠豪, et al.,“基于梯度的结构相似度的图像质量评价方法”,华南理工大学学报:自然科学版, 2006, 34(009), 22–25.
[37] Gao X., Lu W., Li X., et al.,“Wavelet-based contourlet in quality evalua-tion of digital images”, Neurocomputing, 2008, 72(1-3), 378–385.
[38]朱大龙and明军,“基于结构失真的图像质量评价方法的研究”,计算机技术与发展, 2006, 16(003), 56–57.
[39] Andre T., Antonini M., Barlaud M., et al.,“Entropy-based distortion mea-sure and bit allocation for wavelet image compression”, IEEE Transactionson Image Processing, 2007, 16(12), 3058–3064.
[40] Ginesu G., Massidda F., and Giusto D.,“A multi-factors approach forimage quality assessment based on a human visual system model”, SignalProcessing: Image Communication, 2006, 21(4), 316–333.
[41] Delgorge C., Rosenberger C., Poisson G., et al.,“Towards a new tool for theevaluation of the quality of ultrasound compressed images”, IEEE transac-tions on medical imaging, 2006, 25(11), 1502–1509.
[42]韦学辉,李均利,陈刚,“基于多元线性回归的图像质量评价方法”,中国图象图形学报, 2008, 13(11), 2123–2131.
[43] Itti L. and Koch C.,“Computational modelling of visual attention”, NatureReviews Neuroscience, 2001, 2(3), 194–203.
[44] Oliva A., Torralba A., Castelhano M., et al.,“Top-down control of visualattention in object detection”, Image Processing, 2003. ICIP 2003. Pro-ceedings. 2003 International Conference on, volume 1, 2003.
[45] Kadiyala V., Pinneli S., Larson E., et al.,“Quantifying the perceived in-terest of objects in images: e?ects of size, location, blur, and contrast”,Proceedings of SPIE, volume 6806, 2008, 68060S.
[46] Slowe T. and Marsic I.,“Saliency-based visual representation for compres-sion”, Image Processing, 1997. Proceedings., International Conference on,volume 2, 1997.
[47] Menegaz G. and Zambon R.,“Towards a semantic-driven metric for imagequality”, IEEE International Conference on Image Processing, 2005. ICIP2005, volume 3, 2005.
[48] Ninassi A., Le Meur O., Le Callet P., et al.,“Does where you Gaze on anImage A?ect your Perception of Quality? Applying Visual Attention to Im-age Quality Metric”, IEEE International Conference on Image Processing,2007. ICIP 2007, volume 2, 2007.
[49] Larson E., Vu C., and Chandler D.,“Can visual fixation patterns improveimage fidelity assessment?”, 15th IEEE International Conference on ImageProcessing, 2008. ICIP 2008, 2008, 2572–2575.
[50] Sadaka N., Karam L., Ferzli R., et al.,“A no-reference perceptual imagesharpness metric based on saliency-weighted foveal pooling”, 15th IEEE In-ternational Conference on Image Processing, 2008. ICIP 2008, 2008, 369–372.
[51] Lu Z., Lin W., Ong E., et al.,“Perceptual-quality significance map (PQSM)and its application on video quality distortion metrics”, IEEE Interna-tional conference on acoustics speech and signal processing, volume 3, 2003,III:617–620.
[52] Barland R. and Saadane A.,“Blind Quality Metric using a PerceptualImportance Map for JPEG-20000 Compressed Images”, 2006 IEEE Inter-national Conference on Image Processing, 2006, 2941–2944.
[53]马文波,赵保军,任宏亮, et al.,“基于小波频带划分及CSF特性的图像质量评价方法”,激光与红外, 2007, 37(007), 687–690.
[54] Zhang M. and Mou X.,“A psychovisual image Quality Metric based onmulti-scale Structure Similarity”, 15th IEEE International Conference onImage Processing, 2008. ICIP 2008, 2008, 381–384.
[55] Ninassi A., Le Meur O., Le Callet P., et al.,“Which Semi-Local VisualMasking Model For Wavelet Based Image Quality Metric?”, Proc. IEEEInternational Conference on Image Processing (to be appeared in), 2008.
[56] Channappayya S., Bovik A., Caramanis C., et al.,“Design of linear equal-izers optimized for the structural similarity index”, IEEE Transactions onImage Processing, 2008, 17(6), 857–872.
[57] Brooks A., Zhao X., and Pappas T.,“Structural similarity quality metricsin a coding context: exploring the space of realistic distortions”, IEEETransactions on Image Processing, 2008, 17(8), 1261–1273.
[58] Ghanem B., Resendiz E., and Ahuja N.,“Segmentation-based PerceptualImage Quality Assessment (SPIQA)”, 15th IEEE International Conferenceon Image Processing, 2008. ICIP 2008, 2008, 393–396.
[59] Dixon T., Canga E., Nikolov S., et al.,“Selection of image fusion qualitymeasures: objective, subjective, and metric assessment”, Journal of theOptical Society of America A, 2007, 24(12), 125–135.
[60] Grasruck M., Suess C., Stierstorfer K., et al.,“Evaluation of image qualityand dose on a ?at-panel CT-scanner”, Proceedings of SPIE, volume 5745,2005, 179.
[61] Vite C., Mangini M., Strocchi S., et al.,“Dosimetric and image qualityassessment of di?erent acquisition protocols of a novel 64-slice ct scanner”,Proceedings of SPIE, volume 6142, 2006, 61423G.
[62] Londt J., Shreter U., Vass M., et al.,“Dose reduction of up to 89% whilemaintaining image quality in cardiovascular ct achieved with prospectiveecg gating”, Proceedings of SPIE, volume 6510, 2007, 65102S.
[63] McGarry C., Grattan M., and Cosgrove V.,“Optimization of image qualityand dose for Varian aS500 electronic portal imaging devices (EPIDs)”,Physics in Medicine and Biology, 2007, 52(23), 6865–6878.
[64] Watson A., Hu J., and McGowan III J.,“Digital video quality metric basedon human vision”, Journal of Electronic Imaging, 2001, 10, 20.
[65] Wang Z., Lu L., and Bovik A.,“Video quality assessment based on struc-tural distortion measurement”, Signal processing: Image communication,2004, 19(2), 121–132.
[66] Ndjiki-Nya P., Barrado M., and Wiegand T.,“E?cient Full-Reference As-sessment of Image and Video Quality”, IEEE International Conference onImage Processing, 2007. ICIP 2007, volume 2, 2007.
[67] Fullerton M., Woods R., Vera-Diaz F., et al.,“Measuring perceived videoquality of MPEG enhancement by people with impaired vision”, Journalof the Optical Society of America A, 2007, 24(12), 174–187.
[68] Varadarajan S. and Karam L.,“An improved perception-based no-referenceobjective image sharpness metric using iterative edge refinement”, 15thIEEE International Conference on Image Processing, 2008. ICIP 2008,2008, 401–404.
[69] Seshadrinathan K. and Bovik A.,“Unifying analysis of full reference imagequality assessment”, 15th IEEE International Conference on Image Pro-cessing, 2008. ICIP 2008, 2008, 1200–1203.
[70] Reibman A. and Poole D.,“Characterizing packet-loss impairments in com-pressed video”, IEEE International Conference on Image Processing, 2007.ICIP 2007, volume 5, 2007.
[71] Gel’Fand I. and Yaglom A.,“Calculation of the amount of informationabout a random function contained in another such function”, Amer. Math.Soc. Translat., II Ser, 1959, 12, 199–246.
[72] Wainwright M. and Simoncelli E.,“Scale mixtures of Gaussians and thestatistics of natural images”, Advances in neural information processingsystems, 2000, 12(1), 855–861.
[73] Sheikh H.R., Wang Z., Bovik A.C., et al.,“Image and video quality assess-ment research at LIVE”, .
[74] Chandler D.M. and Hemami S.S.,“VSNR: A wavelet-based visual signal-to-noise ratio for natural images”, IEEE Transactions on Image Processing,2007, 16(9), 2284–2298.
[75] Gaubatz M. and Hemami S.,“On the nearly scale-independent rank be-havior of image quality metrics”, 15th IEEE International Conference onImage Processing, 2008. ICIP 2008, 2008, 701–704.
[76] Rohaly A.M., Libert J., Corriveau P., et al.,“Final report from the videoquality experts group on the validation of objective models of video qualityassessment”, 2003.
[77] Borga M., of Electrical Engineering D., and i Link¨oping U., Learningmultidimensional signal processing, Department of Electrical Engineering,Link¨oping University, 1998.
[78] Melzer T., Reiter M., and Bischof H.,“Appearance models based on kernelcanonical correlation analysis”, Pattern recognition, 2003, 36(9), 1961–1971.
[79] Yu S.,“Direct blind channel equalization via the programmable canonicalcorrelation analysis”, Signal Processing, 2001, 81(8), 1715–1724.
[80] Fidler S., Skocaj D., and Leonardis A.,“Combining reconstructive anddiscriminative subspace methods for robust classification and regression bysubsampling”, IEEE transactions on pattern analysis and machine intelli-gence, 2006, 28(3), 337–350.
[81]孙权森,曾生根,王平安, et al.,“典型相关分析的理论及其在特征融合中的应用”,计算机学报, 2005, 28(009), 1524–1533.
[82] Hotelling H.,“RELATIONS BETWEEN TWO SETS OF VARIATES*”,1936.
[83] Sun Q., Zeng S., Liu Y., et al.,“A new method of feature fusion andits application in image recognition”, Pattern Recognition, 2005, 38(12),2437–2448.
[84] Sampat M., Wang Z., Markey M., et al.,“Measuring intra-and inter-observer agreement in identifying and localizing structures in medical im-ages”, 2006 IEEE International Conference on Image Processing, 2006,81–84.
[85] Chen G., Yang C., and Xie S.,“Gradient-based structural similarity forimage quality assessment”, 2006 IEEE International Conference on ImageProcessing, 2006, 2929–2932.
[86] Wang Z. and Simoncelli E.,“Reduced-reference image quality assessmentusing a wavelet-domain natural image statistic model”, Proc. of SPIE Hu-man Vision and Electronic Imaging, volume 5666, 2005, 149–159.
[87] Candes E., Ridgelets: theory and applications, Stanford University, 1998.
[88] Candes E.,“Monoscale ridgelets for the representation of images withedges”, Dept. Statist., Stanford Univ., Stanford, CA, Tech. Rep, 1999.
[89]焦李成and谭山,“图像的多尺度几何分析:回顾和展望”,电子学报, 2004,31(B12), 1975–1981.
[90] Donoho D.,“Sparse components analysis and optimal atomic decomposi-tion”, Constructive Approximation, 2001, 17, 353–382.
[91] Do M. and Vetterli M.,“Contourlets: a new directional multiresolutionimage representation”, Signals, Systems and Computers, 2002. ConferenceRecord of the Thirty-Sixth Asilomar Conference on, volume 1, 2002.
[92] da Cunha A., Zhou J., and Do M.,“Nonsubsampled contourlet transform:filter design and applications in Denoising”, IEEE International Conferenceon Image Processing, Genoa, Italy, volume 11, 2005, 14.
[93] Zhou J., Cunha A., and Do M.,“Nonsubsampled contourlet transform:construction and application in enhancement”, Proceeding of IEEE inter-national conference on Image processing, volume 1, 2005, 469–472.
[94] Eslami R. and Radha H.,“On low bit-rate coding using the contourlettransform”, ASILOMAR CONFERENCE ON SIGNALS SYSTEMS ANDCOMPUTERS, volume 2, Citeseer, 2003, 1524–1528.
[95] Eslami R. and Radha H.,“Wavelet-based contourlet transform and its ap-plication to image coding”, proc. of IEEE International Conference on Im-age Processing, 2004.
[96] Candes E. and Donoho D.,“Curvelets―a surprisingly e?ective nonadaptiverepresentation for objects with edges. Curves and Surfaces, 105–120. Editedby C. Rabut, A. Cohen, and LL Schumaker”, 2000.
[97] Burt P. and II E.,“Adelson,”The Laplacian pyramid as a compact imagecode,””, IEEE Trans. Commun, 1983, 31(4), 532–540.
[98] Do M.N., Directional multi-resolution Image Representation, Ph.D. thesis,Department of Communication Systems, Swiss Federal Institute of Tech-nology Lausanne, 2001.
[99]庞建新,图像质量客观评价的研究, Ph.D. thesis,中国科技大学, 2008.
[100] Lu W., Gao X., Li X., et al.,“An image quality assessment metric basedcontourlet”, 15th IEEE International Conference on Image Processing,2008. ICIP 2008, 2008, 1172–1175.
[101] Levine M.W. and Shefner J.M., Fundamentals of sensation and perception,Brooks/Cole Pub Co, 1991.
[102] Van den Branden Lambrecht C.J. and Verscheure O.,“Perceptual qual-ity measure using a spatio-temporal model of the human visual system”,Proceedings-SPIE the international society for optical engineering, Interna-tional Society for Optical Engineering, 1996, 450–461.
[103] Eisenberg R. (ed.), Radiology: An illustrated history., chapter Injury andProtection., 1992, 156–182.
[104] Brasch R., Boyd D., and Gooding C.,“Computed tomographic scanning inchildren: comparison of radiation dose and resolving power of commercialCT scanners”, American Journal of Roentgenology, 1978, 131(1), 95.
[105] Brenner D., Elliston C., Hall E., et al.,“Estimated risks of radiation-induced fatal cancer from pediatric CT”, American Journal of Roentgenol-ogy, 2001, 176(2), 289.
[106] Liu X., Primak A., Yu L., et al.,“Quantitative evaluation of noise reductionalgorithms for very low dose renal CT perfusion imaging”, Proceedings ofSPIE, volume 7258, 2009, 72581T.
[107] Rivetti S., Lanconelli N., Bertolini M., et al.,“Performance evaluation of adirect computed radiography system by means of physical characterizationand contrast detail analysis”, Proceedings of SPIE, volume 6510, 2007,65104M.
[108] Fan J., Dong F., Sainath P., et al.,“Image quality evaluation of a lightspeedCT750 HD computed tomography system”, Proceedings of SPIE, volume7258, 2009, 72584S.
[109] Vite C., Mangini M., Strocchi S., et al.,“Dosimetric and image qualityassessment of di?erent acquisition protocols of a novel 64-slice CT scanner”,Proceedings of SPIE, volume 6142, 2006, 61423G.
[110] Londt J., Shreter U., Vass M., et al.,“Dose reduction of up to 89% whilemaintaining image quality in cardiovascular CT achieved with prospectiveECG gating”, Proceedings of SPIE, volume 6510, 2007, 65102S.
[111] Strocchi S., Vite C., Novario R., et al.,“Computed tomography qualityindexes: evaluation experience”, Proceedings of SPIE, volume 7258, 2009,72584T.
[112] Leong D. and Brennan P.,“Optimizing image quality using statistical mul-tivariate optimization methodology using desirability functions”, Proceed-ings of SPIE, volume 7258, 2009, 72584U.
[113] Schopphoven S., Faulkner K., and Busch H.,“Assessment of patient or-gan dose in CT virtual colonoscopy for bowel cancer screening”, RadiationProtection Dosimetry, 2008.
[114] Li X., Samei E., Segars W., et al.,“Patient-specific dose estimation forpediatric abdomen-pelvis CT”, Proceedings of SPIE, volume 7258, 2009,725804.
[115] Brisse H., Brenot J., Pierrat N., et al.,“The relevance of image quality in-dices for dose optimization in abdominal multi-detector row CT in children:experimental assessment with pediatric phantoms”, Physics in Medicineand Biology, 2009, 54, 1871–1892.
[116] Lee S., Wang J., Liu S., et al.,“Evaluation of dose–image-quality optimiza-tion in digital chest radiography”, Nuclear Inst. and Methods in PhysicsResearch, A, 2007, 580(1), 544–547.
[117] Yamamoto S., Horiuchi T., Sekiguchi J., et al.,“Design of a DICOM image-based program for estimating patient exposure dose in computed tomogra-phy”, Technology and Health Care, 2007, 15(2), 147–156.
[118] Kazama M., Tsukagoshi S., and Okumura M.,“Image quality improvementand exposure dose reduction with the combined use of X-ray modulationand Boost3D”, Proceedings of SPIE, volume 6142, 2006, 61422G.
[119] Donnelly L., Emery K., Brody A., et al.,“Minimizing radiation dose forpediatric body applications of single-detector helical CT: strategies at alarge Children’s Hospital.”, AJR. American journal of roentgenology, 2001,176(2), 303.
[120]王学礼,“CT图像质量评价技术中若干问题的探讨”, CT理论与应用研究, 2003, 12(003), 46–52.
[121]王学民,沈克涵编著,医学成像系统,清华大学出版社, 2006.
[122] Cosman P., Gray R., and Olshen R.,“Evaluating quality of compressedmedical images: SNR, subjectiverating, and diagnostic accuracy”, volume82, 1994, 919–932.
[123] Cosman P., Tseng C., Gray R., et al.,“Tree-structured vector quantizationof CT chest scans: image quality and diagnostic accuracy”, IEEE transac-tions on medical imaging, 1993, 12(4), 727–739.
[124] Young K., Alsager A., Dance D., et al.,“Predicting contrast detail perfor-mance from objective measurements in digital mammography”, Proceedingsof SPIE, volume 7258, 2009, 72581C.