Super-Resolution Using Adaptive Selectivity Representation

Super-Resolution Using Adaptive Selectivity Representation

In this chapter, we discuss a novel framework for super-resolution (SR) and iterative interpolation
method based on wavelet and an adaptive selectivity representation. This representation is defined
by combining of laplacian pyramid and a multiselectivity decomposition. The result is new tight
frame for each angular selectivity level. This selectivity level can be adapted locally to the content of
the image for each scale; so it can be seen as an adaptive selectivity representation, which present
adaptively isotropic, directional and intermediary features in images. The Experimental results
demonstrate the effectiveness of the proposed approach.

Author(s) Details
Mohamed El Aallaoui
Hassan II University, Casablanca, Morocco.

View Book :- http://bp.bookpi.org/index.php/bpi/catalog/book/234

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