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Sensors 2015, 15(5), 10923-10947; doi:10.3390/s150510923

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

1
Department of Electrical Engineering, COMSATS Institute of Information Technology, Park Road, Chak Shahzad, Islamabad 44000, Pakistan
2
School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
3
Halliburton Worldwide Limited, Islamabad 44000, Pakistan
4
Department of Electrical Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 24 March 2015 / Revised: 29 April 2015 / Accepted: 30 April 2015 / Published: 8 May 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3549 KB, uploaded 8 May 2015]   |  

Abstract

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. View Full-Text
Keywords: multi-focus image fusion; multi-exposure image fusion; signal decomposition; multivariate empirical mode decomposition; multiresolution analysis; non-subsampled contourlet transform multi-focus image fusion; multi-exposure image fusion; signal decomposition; multivariate empirical mode decomposition; multiresolution analysis; non-subsampled contourlet transform
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rehman, N.U.; Ehsan, S.; Abdullah, S.M.U.; Akhtar, M.J.; Mandic, D.P.; McDonald-Maier, K.D. Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition. Sensors 2015, 15, 10923-10947.

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