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Compressive imaging reconstructs the original signal by searching through the feasible space for the solution with maximum compactness under a known frame or dictionary. With the extent of available optimization tools, the recovery performance mainly relies on the power of dictionary to sparsely represent the data. Universal dictionaries can be trained from a corpus of natural images or they can be designed through mathematical modeling. However, a problem with universal dictionaries is thatdoi:10.1109/globalsip.2013.6737070 dblp:conf/globalsip/AghagolzadehR13a fatcat:u2rsdfctjnal3dsyl6vrn5giie