Abstract
Effective sparse representation of X-ray medical images within the context of data reduction is considered. The proposed framework is shown to render an enormous reduction in the cardinality of the data set required to represent this class of images at very good quality. The goal is achieved by (1) creating a dictionary of suitable elements for the image decomposition in the wavelet domain and (2) applying effective greedy strategies for selecting the particular elements, which enable the sparse decomposition of the wavelet coefficients. The particularity of the approach is that it can be implemented at very competitive processing time and low memory requirements.
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