Sparse approximation using fast matching pursuit

Matching pursuit based on geometric dictionary has shown to be a powerful tool for sparse image representation. The main obstacle to its application in real world is the computational complexity. In this paper, a modified algorithm is presented to address this issue. The dictionary with anisotropic refinement atoms is used to provide the approximation ability. Meanwhile the pursuit implementation is significantly speeded up by employing both sequential and parallel techniques. Experimental results show that compared to the latest matching pursuit approach, the proposed algorithm offers a speedup of 27.7-36.7 while maintaining the approximation quality. It is very promising for flexible image coding at low bit rate.

[1]  Pascal Frossard,et al.  Low-rate and flexible image coding with redundant representations , 2006, IEEE Transactions on Image Processing.

[2]  Pascal Frossard,et al.  Efficient image representation by anisotropic refinement in matching pursuit , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[3]  Avideh Zakhor,et al.  Very low bit-rate video coding based on matching pursuits , 1997, IEEE Trans. Circuits Syst. Video Technol..

[4]  P. Frossard,et al.  Tree-Based Pursuit: Algorithm and Properties , 2006, IEEE Transactions on Signal Processing.

[5]  Rémi Gribonval,et al.  Fast matching pursuit with a multiscale dictionary of Gaussian chirps , 2001, IEEE Trans. Signal Process..

[6]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[7]  P. Vandergheynst,et al.  A Matching Pursuit Full Search Algorithm for Image Approximations , 2004 .

[8]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

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

[10]  Pascal Frossard,et al.  The M-term pursuit for image representation and progressive compression , 2005, IEEE International Conference on Image Processing 2005.