Abstract
In traditional compressed sensing MRI methods, single sparsifying transform limits the reconstruction quality because it cannot sparsely represent all types of image features. Based on the principle of basis pursuit, a method that combines sparsifying transforms to improve the sparsity of images is proposed. Simulation results demonstrate that the proposed method can well recover different types of image features and can be easily associated with total variation.
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