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Robust Orientation Diffusion Via PCA Method and Application to Image Super-Resolution Reconstruction

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 4688))

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Abstract

An efficient method with systems of nonlinear partial difference equations (PDE) for image super-resolution was presented, in which geometric edge orientation estimated by the principle component analysis (PCA) method. Using the local and contextual discontinuity of image pixel’s neighborhood, the robust orientation diffusion processes were exploited to enhance the sharpness of edges without incurring any additional artifacts. We also discuss the connection between the new PDE framework and the classical variational PDE method. Furthermore, the implementation scheme of the proposed approach was given by a novel finite difference method. Simulation results show that the proposed approach exhibited better performance than the standard linear interpolation and classical PDE approaches, particularly in the edge regions.

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China

    Liang Xiao, Zhihui Wei & Huizhong Wu

Authors
  1. Liang Xiao

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  2. Zhihui Wei

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  3. Huizhong Wu

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Editor information

Kang Li Minrui Fei George William Irwin Shiwei Ma

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© 2007 Springer-Verlag Berlin Heidelberg

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Xiao, L., Wei, Z., Wu, H. (2007). Robust Orientation Diffusion Via PCA Method and Application to Image Super-Resolution Reconstruction. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_77

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