基于BP神经网络的超分辨率图像重建

Abstract

Abstract: The reconstruction of the super-resolution image based on neural networks(NN) is proposed to resolve the problem of image low spatial resolution because of the limitation of imaging devices.An error back-propagation(BP) algorithm is used to learn and train sample images in order to combine the redundancy information of low spatial resolution images sequences.Learning samples are acquired according to the image observation model.Vector mapping is established to speed up the convergence of NN.Simulation and generalization tests are carried on the well-trained NN respectively,and the reconstruction results with higher spatial resolution images verify the effectiveness and validity of BPNN based on vector mapping in the reconstruction of the super-resolution image.

CLC Number:

TP183

ZHU Fu-zhen, LI Jin-zong, LI Dong-dong. Reconstruction of super-resolution image based on BP neural network[J]. Journal of Systems Engineering and Electronics, 2009, 31(7): 1746-1749,1781.

References

[1] Sung Cheol Park,Min Kyu Park,Moon Gi Kang.Super-resolution image reconstruction; A technical overview[J].IEEE Signal Processing Magazine,2003,21:1053-5888.
[2] Huang T S,Tsai R Y.Multi-frame image restoration and regis-trationCC]//Advances in Computer Vision and Image Processing,1984(1):317-339.
[3] Kim S P,Bose N K,Valenzuela H M.Recursive reconstruction of high resolution image from noisy undersampled multiframes[J].IEEE Trans.on Acoust,Speech,Signal Processing,1990,6(38),1013-1027.
[4] Ur H,Gross D.Improved resolution from subpixel shifted pic tures[J].CVGIP:Graphical Models Image Processing,1992,2(54):181-186.
[5] Irani M,Peleg S.Improving resolution by image registration[J].CVGIP:Graphical Model Image Processing,1991,3(53):231-239.
[6] Cheeseman P,Kanefsky B,Kraft R,et al.Superresolved sur-face reconstruction from multiple images[R].NASA Technical Report FIA,1994:94-12.
[7] Tom B C,Katsaggelos A K.Reconstruction of a high-resolution image by simultaneous registration,restoration,and interpola-tion of low-resolution images[C]//IEEE International Confer-ence Image Processing,Washington DC,1995.
[8] Patti A J,Sezan M I,Tekalp A M.superresolution video recon-struction with arbitrary sampling lattices and nonzero aperture time[J].IEEE Trans.on Image Processing,1997,6(8):1064-1076.
[9] Patti A J,Altunbasak Y.Artifact reduction for set theoretic superresolution image reconstruction with edge adaptive constraints and higher-order interpolants[J].IEEE Trans.on Image Processing,2001,10(1):179-186.
[10] Elad M,Feuer A.superresolution restoration of an image sequence:adaptive filtering approach[J].IEEE Trans.on Image Processing,1999,6(3):387-395.
[11] 刘梅,刘慧念,刘伟东,等.一种基于神经网络的超分辨率图像重构方法[J].哈尔滨工业大学学报,2003,35(6):707-710.
[12] Sun Y.Hopfield neural network based on algorithms for image restoration and reconstruction-Part I:Algorithms and simulations[J].IEEE Trans.on Signal Processing,2000,48(7):2105-2118.
[13] Yao Lu,Minoru Inamura,Maria del Carmen Valdes.Super resolution of the undersampled and subpixel shifted image sequence by a neural network[J].Imaging Systems and Technology,2004,1(14):8-15.
[14] 许东,吴铮.基于Matlab的系统分析与设计--神经网络[M].西安:西安电子科技大学出版社,2003.
[15] Tom M Mitchell.机器学习[M].曾华军,张银奎,译.北京:机械工业出版社,2003.

Reconstruction of super-resolution image based on BP neural network

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