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结合NSCT和改进BP网络的超分辨率图像重建 被引量:2

Super-resolution reconstruction of image via NSCT and improved BP network
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摘要 鉴于非下采样Contourlet变换(NSCT)系数包含原始图像各方向的所有细节信息,以及改进BP神经网络高度非线性映射的快速收敛和准确性,提出一种应用NSCT和改进BP神经网络的超分辨率图像重建算法。分别提取模拟超分辨率图像与相应低分辨率图像各方向子带的NSCT系数进行BP神经网络高度非线性映射训练,直至稳定收敛,并利用该网络实现超分辨率图像重建。实验结果表明该算法在很好保留图像细节的同时极大地降低网络重建复杂度,提高了重建的准确率,重建效果得到明显改进。 This paper presents a new learning based super-resolution of image by introducing the Nonsubsampled Contourlet Transform (NSCT) and improved BP neural network.NSCT can recover the detail information better, as the improved BP can simulate the highly nonlinear, which is fast convergence and accuracy. For both super-resolution image and low-resolution image, it extracts the Contourlet coefficients of each sub-band training the improved BP network, then using the stable and restraining network realizes super-resolution reconstruction of image. The results show that this method is able to preserve the details of original image better and reduce the complexity of the network reconstruction at the same time, raise the accuracy.get significantly improved in reconstruction results.
出处 《计算机工程与应用》 CSCD 2012年第20期195-199,共5页 Computer Engineering and Applications
关键词 超分辨率重建 非下采样CONTOURLET变换 改进BP神经网络 super-resolution reconstruction nonsubsampled Contourlet transform improved BP neural network
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参考文献10

  • 1Elad M.A fast super-resolution reconslruction algorithm forpure translational motion and common space-invariant blur[J]. IEEE Transactions on Image Processing,2001,10(8).
  • 2范立,侯强,李翠玉,李娟.超分辨率图像重构分层迭代ICA算法研究[J].武汉理工大学学报(信息与管理工程版),2009,31(3):358-360. 被引量:3
  • 3Yang Jianchao.Image super-resolution via sparse repre- sentation[J].IEEE Transactions on Image Processing, 2010, 19(11) :2861-2873.
  • 4Sun Y.Hopfield neural network based algorithms for image restoration and reconstruction part 1 algorithms and sim- ulations[J].IEEE Trans on Signal Processing, 2000, 48 (7) :2105-2118.
  • 5Jiji C V, Chaudhuri S.Single-frame images super-resolution through contourlet learning[J].EURASIP Journal on Applied Signal Processing,2006: 1-11.
  • 6Do M N, Vetterli M.Contourlet, beyond wavelets[M].[S.1.] : Academic Press,2002 : 1-27.
  • 7Cunha A L, Zhou Jianping, Do M N.The nonsubsarnpled Contourlet transform: theory, design, and applications[J]. IEEE Trans on Image Processing, 2006, 15 (10) : 3089-3101.
  • 8Yang J, Ou H, Zhou L.Research on the evaluation methods of bid of construction project based on improved BP neural network[J].WSEAS Transactions on Computers, 2010, 1(9):1109-2750.
  • 9Cierniak R.A statistical appraoch to image reconstruction from projections problem using recurrent neural network[C]// LNCS 6353,2010,9(15) : 138-141.
  • 10吴炜,杨晓敏,陈默,何小海,郑丽贤.基于Contourlet变换的人脸图像超分辨率技术研究[J].光电子.激光,2009,20(5):694-697. 被引量:8

二级参考文献21

  • 1朱健翔,苏光大,李迎春.结合Gabor特征与Adaboost的人脸表情识别[J].光电子.激光,2006,17(8):993-998. 被引量:48
  • 2ZHANG R,TSAI P S, CRYER J E. Shape from shading:a survey [ J ]. IEEE Transactions Pattern Analysis and Machine Intelligence, 1999,21 ( 8 ) :690 - 706.
  • 3LIN Z C,SHUM H Y. Fundamental limits of reconstruction- based super -resolution algorithms under local translation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26( 1 ) :83 - 97.
  • 4TANAKA M, OKUTOMI M. Theoretical analysis on reconstruction - based super - resolution for an arbitrary PSF[J]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005,25 ( 2 ) : 947 - 954.
  • 5BAKER S, KANADE T. Limits on super -resolution and how to break them[ J]. IEEE Transactions Pattern Analysis and Machine Intelligence ,2002,24 (9) : 1167 - 1183.
  • 6van Ouwerkerk J D.Image super-resolution survey[a].Image and Vision Computing,2006,24:1039-1052.
  • 7Sung Cheol Park,Min Kyn Park,Moon Gi Kang,Super-resolution image reconstruction:A technical overview[J].IEEE Signal Processing Magazine,2003,20(3) 21-36.
  • 8Freeman W T,Pasztor E C,Carmichael O T.Learning low-level vision[J].International Journal on Compter V.isiono2000,40(1):25-47.
  • 9SUN Jian,ZHENG Nan-Ning.Image hallucination with primal sketch priors[A].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2003(2):729-736.
  • 10Baker S,Kanade T.Limits on super-resolution and how to break them[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,2,4(9):1167-1183.

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