Adaptive MAP High-Resolution Image Reconstruction Algorithm Using Local Statistics 


Vol. 31,  No. 12, pp. 1194-1200, Dec.  2006


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  Abstract

In this paper, we propose an adaptive MAP (Maximum A Posteriori) high-resolution image reconstruction algorithm using local statistics. In order to preserve the edge information of an original high-resolution image, a visibility function defined by local statistics of the low-resolution image is incorporated into MAP estimation process, so that the local smoothness is adaptively controlled. The weighted non-quadratic convex functional is defined to obtain the optimal solution that is as close as possible to the original high-resolution image. An iterative algorithm is utilized for obtaining the solution, and the smoothing parameter is updated at each iteration step from the partially reconstructed high-resolution image is required. Experimental results demonstrate the capability of the proposed algorithm.

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  Cite this article

[IEEE Style]

K. Kim, W. Song, M. Hong, "Adaptive MAP High-Resolution Image Reconstruction Algorithm Using Local Statistics," The Journal of Korean Institute of Communications and Information Sciences, vol. 31, no. 12, pp. 1194-1200, 2006. DOI: .

[ACM Style]

Kyung-Ho Kim, Wonseon Song, and Min-Cheol Hong. 2006. Adaptive MAP High-Resolution Image Reconstruction Algorithm Using Local Statistics. The Journal of Korean Institute of Communications and Information Sciences, 31, 12, (2006), 1194-1200. DOI: .

[KICS Style]

Kyung-Ho Kim, Wonseon Song, Min-Cheol Hong, "Adaptive MAP High-Resolution Image Reconstruction Algorithm Using Local Statistics," The Journal of Korean Institute of Communications and Information Sciences, vol. 31, no. 12, pp. 1194-1200, 12. 2006.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880