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Abstract #2895

Dictionary-Based Sparsification & Reconstruction (DIBSAR)

Berkay Kanberoglu1, Lina J. Karam1, David Frakes1,2

1School of Electrical, Computer & Energy Engineering, Arizona State University, Tempe, AZ, United States; 2School of Biological & Health Systems Engineering, Arizona State University, Tempe, AZ, United States

An alternative method to ABSINTHE (Atlas based sparsification of image and theoretical estimation) is proposed. ABSINTHE achieves sparsification by performing a principle component analysis (PCA) on the aliased undersampled image. Better sparsification can be achieved by using K-SVD. K-SVD provides flexibility of dictionary design parameters which can be important for the image approximation. The proposed method shows that K-SVD is able to reconstruct similar quality images in comparison to the traditional ABSINTHE method while using half the number of basis images required by PCA.