Abstract
In this paper, we propose an adaptive MAP (Maximum A Posteriori) high-resolution image reconstruction algorithm using local statistics. In order to preserve an 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 nonquadratic 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. The smoothing parameter is updated at each iteration step from the partially reconstructed high-resolution image, and therfore no knowledge about of the original high-resolution image is required. Experimental results demonstrate the capability of the proposed algorithm.
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Kim, KH., Shin, Y., Hong, MC. (2005). Adaptive MAP High-Resolution Image Reconstruction Algorithm Using Local Statistics. In: Ho, YS., Kim, HJ. (eds) Advances in Multimedia Information Processing - PCM 2005. PCM 2005. Lecture Notes in Computer Science, vol 3768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11582267_49
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DOI: https://doi.org/10.1007/11582267_49
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