Bidimensional-Multivariate-Empirical-Mode-Decomposition

Matlab codes of Bidimensional Multivariate Empirical Mode Decomposition (BMEMD).

Introduction

BMEMD is a bidimensional and multivariate version of original EMD, which is capable of processing multi-images, such as image fusion, texture analysis and so on. More details about the BMEMD can be referred in our paper Bidimensional Multivariate Empirical Mode Decomposition with Applications in Multi-Scale Image Fusion.

Requirements

• Image Processing Toolbox (installed in Matlab)
• gridfitdir (attanched in this repo., add the path to your Matlab environment)

How to use these codes?

Files and Directories

• bmemd.m
  • main code of proposed BMEMD
• bmemd_fusion.m
  • its application on multi-images fusion, several images are provided at path ./IMG
• Texture_Generate.m
  • the code to generate synthetic texture images in paper

Usages (take only decomposition as the example)

x: [n, h, w], a non-int array
q: a cell of length Q, the number of IMFs, and each array in the cell 
   share the same size with x representing the corresponding IMF of x

1. q=bmemd(x)
2. q=bmemd(x, ndir), here, ndir is the number of projections
3. q=bmemd(x,ndir,stop_crtit), stop_crtit means stopping conditions, can be choosen from 'stop' and 'fix_h'. If 'stop', the default parameter is [0.01, 0.1, 0.01], ohtherwise, fix_h=2
4. q=bmemd(x,ndir,'stop',[x1,x2,x3]), [x1,x2,x3] is parameter of stop criteria
5. q=bmemd(x,nir,'fix_h',fix_h)

Reference

[1] N. Rehman and D. P. Mandic,, "Multivariate empirical mode decomposition," Proc. R. Soc. A, vol.466, no. 2117, pp. 1291-1302, 2010.
[2] T. Tanaka and D. P. Mandic, “Complex empirical mode decomposition,” IEEE Signal Process. Lett., vol. 14, no. 2, pp. 101–104, Feb. 2007.
[3] G. Rilling, P. Flandrin, P. Gonalves, and J. M. Lilly, “Bivariate empirical mode decomposition,” IEEE Signal Process. Lett., vol. 14, no.12, pp. 936–939, Dec. 2007.
[4] N. Rehman and D. P. Mandic, “Empirical mode decomposition for trivariate signals,” IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1059–1068, Mar. 2010.
[5] J. C. Nunes, S. Guyot, and E. Delechelle, “Texture analysis based on local analysis of the bidimensional empirical mode decomposition,” Mach. Vis. Appl., vol. 16, no. 3, pp. 177–188, May 2005.

Citation

@article{Xia2019BMEMD, 
	author		= {Y. Xia and B. Zhang and W. Pei and D. P. Mandic}, 
	journal		= {IEEE Access},
	title		= {Bidimensional Multivariate Empirical Mode Decomposition With Applications in Multi-Scale Image Fusion},
	year		= {2019},
	volume		= {7}, 
	pages		= {114261-114270}, 
	doi 		= {10.1109/ACCESS.2019.2936030}, 
	ISSN		= {2169-3536}, 
	month		= {Dec.}
}