Design of Incoherent Frames via Convex Optimization

This paper describes a new procedure for the design of incoherent frames used in the field of sparse representations. We present an efficient algorithm for the design of incoherent frames that works well even when applied to the construction of relatively large frames. The main advantage of the proposed method is that it uses a convex optimization formulation that operates directly on the frame, and not on its Gram matrix. Solving a sequence of convex optimization problems allows for the introduction of constraints on the frame that were previously considered impossible or very hard to include, such as non-negativity. Numerous experimental results validate the approach.

[1]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[2]  N. J. A. Sloane,et al.  Packing Lines, Planes, etc.: Packings in Grassmannian Spaces , 1996, Exp. Math..

[3]  Thomas Strohmer,et al.  GRASSMANNIAN FRAMES WITH APPLICATIONS TO CODING AND COMMUNICATION , 2003, math/0301135.

[4]  Robert W. Heath,et al.  Designing structured tight frames via an alternating projection method , 2005, IEEE Transactions on Information Theory.

[5]  Joachim M. Buhmann,et al.  Learning Dictionaries With Bounded Self-Coherence , 2012, IEEE Signal Processing Letters.

[6]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[7]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[8]  Michael Elad,et al.  Optimized Projections for Compressed Sensing , 2007, IEEE Transactions on Signal Processing.

[9]  Vivek K. Goyal,et al.  Quantized Overcomplete Expansions in IRN: Analysis, Synthesis, and Algorithms , 1998, IEEE Trans. Inf. Theory.

[10]  Il-Min Kim,et al.  Existence and construction of noncoherent unitary space-time codes , 2002, IEEE Trans. Inf. Theory.

[11]  Thomas Strohmer,et al.  A note on equiangular tight frames , 2008 .

[12]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[13]  N. T. Thao,et al.  QUANTIZED OVER COMPLETE EXPANSIONS IN RN: ANALYSIS , 1998 .

[14]  Ole Christensen,et al.  Frames and Bases , 2008 .

[15]  Roy D. Yates,et al.  Iterative construction of optimum signature sequence sets in synchronous CDMA systems , 2001, IEEE Trans. Inf. Theory.

[16]  A. Calderbank,et al.  Z4‐Kerdock Codes, Orthogonal Spreads, and Extremal Euclidean Line‐Sets , 1997 .

[17]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[18]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[19]  Aggelos K. Katsaggelos,et al.  Use of tight frames for optimized compressed sensing , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[20]  Mátyás A. Sustik,et al.  On the existence of equiangular tight frames , 2007 .