基于放大曲波基的方向性超分辨率图像重构技术
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摘要
为解决传统图像放大算法边界视觉效果不佳的问题,提出基于二代曲波变换的方向性超分辨率图像重构算法.对图像进行j层曲波分解,利用不同尺度上曲波基的空间比例关系获得放大图像j层分解系数,通过最外层曲波基空间模型可构建(j+1)层放大图像的曲波分解系数,采用新的非线性函数对全部曲波系数进行增强处理,根据曲波分解的方向性,最终可通过曲波重构获得边缘特征较好的放大图像.实验结果表明,基于曲波方向性图像放大算法,可以较好地保留原图的几何特征,增强边缘清晰度;将两幅典型图像放大后的峰值信噪比与经典方法(差值算法)比较分别提升了2.2及0.6 dB.
A super-resolution image reconstruction algorithm was proposed using the 2nd generation curvelet to reduce the edge blur caused by traditional algorithms.In the proposed algorithm,the original image is decomposed into j scales using curvelet.The curvelet coefficients in the j scales of the zoomed-in image are obtained by utilizing the proportionality of curvelet bases between adjacent scales,and the curvelet coefficients in the(j+1)th scale are determined by utilizing the spatial template of curvelet coefficients with the largest scale number.All the curvelet coefficients are processed with a nonlinear function to enhance image quality.The zoomed-in image with fine edges is finally created through curvelet reconstruction because of the good directional characteristic of curvelet.Experiments on two benchmarking images shown that,the proposed algorithm could preserve more image features and edge sharpness,and the peak signal to noise ratios(PSNRs) for the two images increased by 2.2 and 0.6 dB,respectively,compared with those obtained with a traditional interpolation algorithm.
A super-resolution image reconstruction algorithm was proposed using the 2nd generation curvelet to reduce the edge blur caused by traditional algorithms.In the proposed algorithm,the original image is decomposed into j scales using curvelet.The curvelet coefficients in the j scales of the zoomed-in image are obtained by utilizing the proportionality of curvelet bases between adjacent scales,and the curvelet coefficients in the(j+1)th scale are determined by utilizing the spatial template of curvelet coefficients with the largest scale number.All the curvelet coefficients are processed with a nonlinear function to enhance image quality.The zoomed-in image with fine edges is finally created through curvelet reconstruction because of the good directional characteristic of curvelet.Experiments on two benchmarking images shown that,the proposed algorithm could preserve more image features and edge sharpness,and the peak signal to noise ratios(PSNRs) for the two images increased by 2.2 and 0.6 dB,respectively,compared with those obtained with a traditional interpolation algorithm.
引文
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[15]XULI Z,LAINE A F,GEISER E A.Specklereduction and contrast enhancement of echocardiogramsvia multiscale nonlinear processing[J].IEEETransactions on Medical Imaging,1998,17(4):532-540.
[16]STARCK J L,MURTAGH F,CANDES E J,et al.Gray and color image contrast enhancement by thecurvelet transform[J].IEEE Transactions on ImageProcessing,2003,12(6):706-717.
[17]NEZHADARYA E,SHAMSOLLAHI M B.Imagecontrast enhancement by contourlet transform[C]∥48th International Symposium ELMAR-2006 Focusedon Multimedia Signal Processing and Communications.Zadar:[s.n.],2006:81-84.
[2]鲁恒,李永树,李何超,等.无人机影像数字处理及在地震灾区重建中的应用[J].西南交通大学学报,2010,45(4):533-538.LU Heng,LI Yongshu,LI Hechao,et al.Digitalprocessing of unmanned aerial vehicle image and itsapplication in reconstruction of wenchuan earthquake-hitareas[J].Journal of Southwest Jiaotong Unversity,2010,45(4):533-538.
[3]HARRIS J L.Diffraction and resolving power[J].Journal of the Optical Society of America,1964,54(7):931-936.
[4]于殿泓.图像检测与处理技术[M].西安:西安电子科技大学出版社,2006:74-79.
[5]SCHULTZ R R,STEVENSON R L.A Bayesianapproach to image expansion for improveddefinition[J].IEEE Transactions on Image Processing,1994,3(3):233-242.
[6]IRANI M,PELEG S.Improving resolution by imageregistration[J].Graphical Models and ImageProcessing,1991,53(3):231-239.
[7]PATTI AJ,SEZANMI,MURATTA.Superresolutionvideo reconstruction with arbitrary sampling lattices andnonzero aperture time[J].IEEE Transactions on ImageProcessing,1997,6(8):1064-1076.
[8]张新明,沈兰荪.在小波变换域内实现图像的超分辨率复原[J].计算机学报,2003,26(9):1183-1189.ZHANG Xinming,SHEN Lansun.Super-resolutionrestoration from image sequences in the waveletdomain[J].Chinese Journal of Computers,2003,26(9):1183-1189.
[9]赵书斌,彭思龙.基于小波域HMT模型的图像超分辨率重构[J].计算机辅助设计与图形学学报,2003,15(11):1347-1352.ZHAO Shubin,PENG Silong.Wavelet-domain hmt-based image superresolution[J].Journal of ComputerAided Design&Computer Graphics,2003,15(11):1347-1352.
[10]李元勇.马赫带现象研究[D].天津:天津大学,2008.
[11]XIN L,ORCHARD M T.New edge-directedinterpolation[J].IEEE Transactions on ImageProcessing,2001,10(10):1521-1527.
[12]程光权,成礼智.基于小波的方向自适应图像插值[J].电子与信息学报,2009,31(2):265-269.CHENG Guangquan,CHENG Lizhi.Directionadaptive image Interpolation via wavelet transform[J].Journal of Electronics&Information Technology,2009,31(2):265-269.
[13]CAND S E.New tight frames of curvelets and optimalrepresentations of objects with piecewise-C2singularities[J].Comm.Pure Appl.Math.,2002,57:219-266.
[14]CAND S E,DEMANET L,DONOHO D.Fastdiscrete curvelet transforms[J].Multiscale ModelingSimulation,2005(5):861-899.
[15]XULI Z,LAINE A F,GEISER E A.Specklereduction and contrast enhancement of echocardiogramsvia multiscale nonlinear processing[J].IEEETransactions on Medical Imaging,1998,17(4):532-540.
[16]STARCK J L,MURTAGH F,CANDES E J,et al.Gray and color image contrast enhancement by thecurvelet transform[J].IEEE Transactions on ImageProcessing,2003,12(6):706-717.
[17]NEZHADARYA E,SHAMSOLLAHI M B.Imagecontrast enhancement by contourlet transform[C]∥48th International Symposium ELMAR-2006 Focusedon Multimedia Signal Processing and Communications.Zadar:[s.n.],2006:81-84.
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