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.