A Two-layer Constraint Image Interpolation Model Basing on Both Local and Global Information

Wu Ji-ying; Ruan Qiu-qi

doi:10.3724/SP.J.1146.2006.00878

Volume 30 Issue 1

Jan. 2011

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Article Navigation > Journal of Electronics & Information Technology > 2008 > 30(1): 144-148

Wu Ji-ying, Ruan Qiu-qi. A Two-layer Constraint Image Interpolation Model Basing on Both Local and Global Information[J]. Journal of Electronics & Information Technology, 2008, 30(1): 144-148. doi: 10.3724/SP.J.1146.2006.00878

Citation:

Wu Ji-ying, Ruan Qiu-qi. A Two-layer Constraint Image Interpolation Model Basing on Both Local and Global Information[J]. Journal of Electronics & Information Technology, 2008, 30(1): 144-148. doi: 10.3724/SP.J.1146.2006.00878

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A Two-layer Constraint Image Interpolation Model Basing on Both Local and Global Information

doi: 10.3724/SP.J.1146.2006.00878

Received Date: 2006-06-19
Rev Recd Date: 2006-11-23
Publish Date: 2008-01-19

Abstract

Abstract

In this paper, a novel two-layer constraint image interpolation model is proposed. The novel model interpolates image under the constraint of gradient magnitude in original un-interpolated image. The edge pixel is processed by Partial Differential Equation (PDE). PDE enhances edge and smoothes jaggies in the edge. The pixel in smooth regions of under-interpolated image is processed by Non Local (NL)-means model. NL-means model gets gray value of pixel by weighted averaging global information in image and image is smoothed. Texture image interpolated by the novel model preserves the entire texture pattern; the jaggies in linear structure of texture is smoothed too. It is even proved by theory and experiments that noisy image can be interpolated directly to the required size using this novel model.

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References(1)

References

Hou H S and Andrews H C. Cubic splines for imageinterpolation and digital filtering[J].IEEE Trans. on Acoustics,Speech, Signal Processing.1978, 26(6):508-517[2]Caselles V, Morel J M, and Sbert C. An axiomatic approachto image interpolation[J].IEEE Trans. on Image Processing.1998, 7(3):376-386[3]Morse B and Schwartzwald D. Image magnification usinglevel-set reconstruction. CVPR01,Hawaii, Dec 2001: 333-340.[4]Liu Z X, Wang H J, and Peng S L. An image magnificationmethod using joint diffusion[J].Journal of Computer Scienceand Technology.2004, 19(5):698-707[5]Fu S J. Adaptive image interpolation using coupledbidirectional flow. IEEE Int. Conf. on Image Processing,Genoa, Italy, Sep 2005, 2: II- 970-3.[6]Antoni B, Bartomeu C, and Morel J M. On Image DenoisingMethods. CMLA Preprint, CMLA 2004-15, 2004.[7]Wittman T. Mathematical techniques for image interpolation.Oral Exam. Paper, July 2005.[8]Allebach J and Wong P W. Edge-directed interpolation.IEEE International Conference on Image Processing,Lausanne, Switzerland, Sep 1996: 707-710.[9]Alvarez L and Mazorra L. Signal and image restoration usingshock filters and anisotropic diffusion. SIAM J. NumericalAnalysis, 1994, 31(2): 590-605.[10]邹谋炎. 反卷积和信号复原. 北京: 国防工业出版社, 2001:233-242.Zou Mou-yan. Deconvolution and Signal Recovery. Beijing:National Defence Industry Press, 2001: 233-242.

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