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Image recovery from undersampled data has always been a challenging and fascinating task due to its implicit ill-posed nature and significance accompanied with the emerging compressed sensing (CS) theory. This paper proposes a novel Gradient based Dictionary Learning method for CT image Reconstruction (GradDL-CT), which alleviates the drawback of the popular total variation (TV) regularization by...
Initialization sensitivity usually occurs in dictionary learning algorithm for image decomposition. In this paper, we propose an adaptive dictionary learning algorithm by promoting structural incoherence at the stage of dictionary updating. The structural incoherence based dictionary learning (SIDL) method guides the cartoon and texture parts to be more properly represented by two incoherent dictionaries...
Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for image recovery, which effectively integrates the popular total variation (TV) and dictionary learning technique into the same framework. Specifically,...
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