Baker, C.A., King, K., Liang, D. and Ying, L. (2011) Translational-Invariant Dictionaries for Compressed Sensing in Magnetic Resonance Imaging. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1602-1605.
http://dx.doi.org/10.1109/isbi.2011.5872709

has been cited by the following article:

• TITLE: Sparse Representation by Frames with Signal Analysis

  AUTHORS: Christopher Baker

  KEYWORDS: Compressed Sensing, Total Variation Minimization, l1-Analysis, D-Restricted Isometry Property, Tight Frames

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.7 No.1, February 29, 2016

  ABSTRACT: The use of frames is analyzed in Compressed Sensing (CS) through proofs and experiments. First, a new generalized Dictionary-Restricted Isometry Property (D-RIP) sparsity bound constant for CS is established. Second, experiments with a tight frame to analyze sparsity and reconstruction quality using several signal and image types are shown. The constant is used in fulfilling the definition of D-RIP. It is proved that k-sparse signals can be reconstructed if by using a concise and transparent argument1. The approach could be extended to obtain other D-RIP bounds (i.e. ). Experiments contrast results of a Gabor tight frame with Total Variation minimization. In cases of practical interest, the use of a Gabor dictionary performs well when achieving a highly sparse representation and poorly when this sparsity is not achieved.