Wavelet-Domain High Resolution Image Reconstruction

Li Wen Dong

doi:10.4028/www.scientific.net/AMR.756-759.3309

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Wavelet-Domain High Resolution Image...

Wavelet-Domain High Resolution Image Reconstruction

Abstract:

The aliasing due to subsampling and the blur from the finite detector size can decrease quality of the images and make fine details and structures difficult to interpret. High resolution images can be reconstructed from several adjacent frames in a sequence by reconstruction process. This paper describes the high resolution image reconstruction method based on wavelet domain. In this approach both the image sequences and the degradation operator are presented by orthogonal wavelet with compact support. Experimental results demonstrate that the proposed method is effective to improve image details.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

3309-3312

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.3309

Citation:

Cite this paper

Online since:

September 2013

Authors:

Li Wen Dong

Keywords:

High Resolution, Image Reconstruction, Wavelet Transform (WT)

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References

[1] R.C. Gonzalez, R.E. Woods, Digital Image Processing, Prentice Hall, (2002).

Google Scholar

[2] T.S. Huang, R.Y. Tsay, Multiple frame image restoration and registration, Advances in Computer Vision and Image Processing, vol. 1, pp.317-339, (1984).

Google Scholar

[3] H. Ur and D. Gross, Improved resolution from sub-pixel shifted pictures, CVGIP: Graphical Models and Image Processing, vol. 54, pp.181-186, Mar. (1992).

DOI: 10.1016/1049-9652(92)90065-6

Google Scholar

[4] Irani M, Peleg S, Improving resolution by image registration, GVGCP: Graph Models and Image Processing, pp.231-239, (1991).

DOI: 10.1016/1049-9652(91)90045-l

Google Scholar

[5] Stark. H and Oskoui. P, High resolution image recovery from image-plane arrays, using convex projections;, J. Opt. Soc. Am. A, Vol. 6, p.1715–1726, (1989).

DOI: 10.1364/josaa.6.001715

Google Scholar

[6] Schulz. R. R. and Stevenson. R. L, Extraction of high-resolution frames from video sequences, IEEE Trans. Image Processing, p.996–1 011, (1996).

DOI: 10.1109/83.503915

Google Scholar

[7] Sigeru. Omatu, Hideo. Araki, Image Restoration by Revised e Bayesian-Based, ADVCOMP : The Fifth International Conference on Advanced Engineering Computing and Applications in Sciences, pp.60-65, (2011).

Google Scholar

[8] S Peleg, D. Keren, L Schweitzer, Improving Image Resolution Using Subpixel Motion, Pattern Recognition Letters, pp.223-226, (1987).

DOI: 10.1016/0167-8655(87)90067-5

Google Scholar