Bing Wu1,2, Philip Bones1, Richard Watts3, Rick Millane1
1Electrical and computer engineering, University of Canterbury, Christchurch, Canterbury, New Zealand; 2Brain Imaging and Analysis Center, School of Medicine, Duke University, Durham, NC, United States; 3Physics and Astronomy, University of Canterbury, New Zealand
The success level of compressed sensing (CS) reconstruction is fundamentally limited by the sparsity of the underlying image. A data sorting process can be incorporated in the CS recovery to improve the sparsity of the underlying image based on the knowledge of an image prior estimate. We here show that performing a data sorting effectively incorporates the image prior estimate in the CS reconstruction.