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Super-Resolution with Multiselective Contourlets
Laboratory of Mathematical Engineering (LINMA), Department of Mathematics and Computer Science, Faculty of Sciences, Eljadida, Morocco
Faculté des Sciences Juridiques, économiques et Sociales de Ain Sebaa, Casablanca, Morocco
- 1 Laboratory of Mathematical Engineering (LINMA), Department of Mathematics and Computer Science, Faculty of Sciences, Eljadida, Morocco
- 2 Faculté des Sciences Juridiques, économiques et Sociales de Ain Sebaa, Casablanca, Morocco
American Journal of Computational Mathematics·Volume 02 (2012)·Pages 302–311·Published 18 December 2012·DOI10.4236/ajcm.2012.24041
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Abstract
We introduce a new approach to image super-resolution. The idea is to use a simple wavelet-based linear interpolation scheme as our initial estimate of high-resolution image; and to intensify geometric structure in initial estimation with an iterative projection process based on hard-thresholding scheme in a new angular multiselectivity domain. This new domain is defined by combining of laplacian pyramid and angular multiselectivity decomposition, the result is multiselective contourlets which can capture and restore adaptively and slightly better geometric structure of image. The experimental results demonstrate the effectiveness of the proposed approach.
KeywordsSuper-ResolutionLaplacian PyramidAngular MultiselectivityMultiselective ContourletsAnti-Aliasing FilerSparsity ConstraintIterative Projection
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