Iterative Adaptive Hybrid Image Restoration for Fast Convergence 


Vol. 35,  No. 9, pp. 743-747, Sep.  2010


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  Abstract

This paper presents an iterative adaptive hybrid image restoration algorithm for fast convergence. The local variance, mean, and maximum value are used to constrain the solution space. These parameters are computed at each iteration step using partially restored image at each iteration, and they are used to impose the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed and better performance than typical regularized constrained least squares (RCLS) approach.

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  Cite this article

[IEEE Style]

K. Ko and M. Hong, "Iterative Adaptive Hybrid Image Restoration for Fast Convergence," The Journal of Korean Institute of Communications and Information Sciences, vol. 35, no. 9, pp. 743-747, 2010. DOI: .

[ACM Style]

Kyel Ko and Min-Cheol Hong. 2010. Iterative Adaptive Hybrid Image Restoration for Fast Convergence. The Journal of Korean Institute of Communications and Information Sciences, 35, 9, (2010), 743-747. DOI: .

[KICS Style]

Kyel Ko and Min-Cheol Hong, "Iterative Adaptive Hybrid Image Restoration for Fast Convergence," The Journal of Korean Institute of Communications and Information Sciences, vol. 35, no. 9, pp. 743-747, 9. 2010.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880