基于层次特征的自适应径向基插值图像放大的保真指标
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摘要
图像作为一种重要的信息载体,在生活中不可或缺,如何最大程度地保留和获取图像中的信息自然也成了人们所关心的话题。近年来,径向基函数(RBF)插值成为解决散乱数据插值的一种新的有效方法。径向基函数的图像放大过程中,不同参数取值对图像的放大具有非常大的影响,构造适当的保真指标对图像放大质量的评判和参数取值的研究尤为关键。文中主要建立了基于图像的层次特征和分块矩阵的径向基函数插值的图像放大的保真指标,它由全局失真度和边缘失真度两部分组成,实验结果表明了保真指标定义的有效性,在此基础上研究了MQ、逆MQ,以及Gauss径向基函数参数与图像纹理放大机制的关联程度。
As an important information carrier,image is indispensable in life,and how to retain and acquire the information in the image to the greatest extent has been a big topic for a long time.In recent years,radial basis function(RBF) interpolation has become a new effective method to solve the problem of scattered data interpolation.In the image magnification based on radial basis function,the values of different parameters have a great influence on the magnified ima-ge.The appropriate fidelity index is particularly critical for the image quality evaluation and the study on the parameters.This paper mainly presented the definition of fidelity index for image magnification based on the multilevel feature of image and the radial basis function of the block matrix,which consists of the global distortion index and the edge distortion index.The experimental results show that the definition of fidelity index is effective.Furthermore,the correlations between the parameters of MQ,inverse MQ and the Gauss radial basis functions and the image texture amplification mechanism were studied.
As an important information carrier,image is indispensable in life,and how to retain and acquire the information in the image to the greatest extent has been a big topic for a long time.In recent years,radial basis function(RBF) interpolation has become a new effective method to solve the problem of scattered data interpolation.In the image magnification based on radial basis function,the values of different parameters have a great influence on the magnified ima-ge.The appropriate fidelity index is particularly critical for the image quality evaluation and the study on the parameters.This paper mainly presented the definition of fidelity index for image magnification based on the multilevel feature of image and the radial basis function of the block matrix,which consists of the global distortion index and the edge distortion index.The experimental results show that the definition of fidelity index is effective.Furthermore,the correlations between the parameters of MQ,inverse MQ and the Gauss radial basis functions and the image texture amplification mechanism were studied.
引文
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[3] ZHU S,ZENG B,LIU G,et al.Image interpolation based on non-local geometric similarities[J].IEEE Transactions on Multimedia,2016,18(9):1707-1719.
[4] SUN X,LI X,LI J,et al.Research progress of image super resolution restoration based on deep learning[J].Acta Automatica Sinica,2017,43(5):697-709.
[5] WEICKERT J.Anisotropic Diffusion in Image Processing[J].B.g.teubner Stuttgart,1998,16(1):272.
[6] MOORTHY A K,BOVIK A C.Blind image quality assessment:from natural scene statistics to perceptualquality[J].IEEE Transactions on Image Processing,2011,20(12):3350-3364.
[7] MITTAL A,SOUNDARARAJAN R,BOVIK A C.Making a “Completely Blind” Image Quality Analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212.
[8] HEN H,LI P,ZHANG L,et al.Overview of image super reso- lution reconstruction technology and methods[J].Optical Technique,2009,35(2):194-203.
[9] BLOMGREN P,CHAN T F,MULET P,et al.Total variation image restoration:numerical methods and extensions[C]//International Conference on Image Processing.IEEE,1997:384-387
[10] ZHANG W,MA L,MA L,et al.Learning structure of stereoscopic image for no-reference quality assessment with convolutional neural network[J].Pattern Recognition,2016,59(C):176-187.
[11] WU Z M.Radial basis function scattered data interpolation and the meshless method of numerical solution of PDEs[J].Chinese Journal of Engineering Mathematics,2002,19(2):1-12.
[12] LI C.A Method of Image Magnification Based on Radial Basis Function[J].Journal of Information & Computational Science,2015,12(13):4867-4873.
[13] HOU H S,ANDREWS H.Cubic Splines for Image Interpolation and Digital Filtering[J].IEEE Transactions on Acoustics Speech & Signal Processing,1978,26(6):508-517.
[14] KEYS R.Cubic convolution interpolation for digital image processing[J].IEEE Transactions on Acoustics Speech & Signal Processing,2003,29(6):1153-1160.
[15] CHANG S G,CVETKOVIC Z,VETTERLI M.Locally adaptive wavelet-based image interpolation[J].IEEE Trans Image Process,2006,15(6):1471-1485.
[16] DONG C,LOY C C,HE K,et al.Image SuperResolution Using Deep Convolutional Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(2):295-307.
[17] YAN Q,XU Y,YANG X,et al.Single Image Super-Resolution Based on Gradient Profile Sharpness[J].IEEE Transactions on Image Processing,2015,24(10):3187-3202.
[18] HANG L,WU X.An edge-guided image interpolation algorithm via directional filtering and data fusion[J].IEEE Transactions on Image Processing,2006,15(8):2226-2238.
[19] LI X,ORCHARD M T.New edge-directed interpolation[J].IEEE Trans Image Process,2001,10(10):1521-1527.
[20] JING G,CHOI Y K,WANG J,et al.Gradient guided image interpolation[C]//IEEE International Conference on Image Processing.IEEE,2015:1822-1826.
[21] CHANTA R,SHAJI A,SMITH K,et al.SLIC Superpixels Compared to State of the Art Superpixel Methods[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,34(11):2274-2282.
[22] REEMAN W T,JONES T R,PASZTOR E C.Example Based Super-Resolution[J].Computer Graphics & Applications IEEE,2013,22(2):56-65.
[23] HAN C Y.Improved SLIC imagine segmentation algorithm based on K-means[J].Cluster Computing,2017,20(2):1-7.
[24] YANG C K,PENG L K.Automatic Mood Transferring between Color Images[J].IEEE Computer Graphics & Applications,2008,28(2):52-61.
[25] AN J Q,ZHU G Q.General framework for vector valued interpolants[C]//Proceedings of Third China-Japan Seminar on Numerical Mathematics.Dalian,1997:273-278.
[26] ZHAO Q J,TAN J.Block-based Thiele-like blending rational interpolation [J].Journal of Computational & Applied Mathema-tics,2006,195(1):312-325.
[27] LI C,ZHU X,GU C.Matrix Padé-Type Method for Computing the Matrix Exponential[J].Applied Mathematics,2011,2(2):247-253.
[28] MOLER C,VAN LOAN C.Nineteen Dubious Ways to Compute the Exponential of a Matrix,Twenty-Five Years Later[J].SIAM Review,1978,20(4):801-836.
[29] GU C.Multivariate generalized inverse vector-valued rational interpolants[J].Journal of Computational & Applied Mathema-tics,1997,84(84):137-146.
[30] QIU P.Jump Surface Estimation,Edge Detection,and Image Restoration[J].Publications of the American Statistical Associa-tion,2007,102(478):745-756.
[31] GAO J,HAN Y,GU H.An improved algorithm of no reference image quality assessment[J].Command Control and Simulation,2016,38(4):44-49.
[32] HE K,ZHANG X,REN S,et al.Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,37(9):1904-1916.
[33] XUE W,MOU X,ZHANG L,et al.Blind imagequality assess- ment using joint statistics of gradient magnitude and Laplacian features[J].IEEE Trans Image Process,2014,23(11):4850-4862.
[34] SUN J,QIU P.Jump Detection in Regression Surfaces Using Both First-Order and Second-Order Derivatives[J].Journal of Computational & Graphical Statistics,2007,16(2):289-311.