Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.
Naveed ur Rehman,Shoaib Ehsan,Syed Muhammad Umer Abdullah,Muhammad Jehanzaib Akhtar,Danilo P. Mandic,Klaus D. McDonald-Maier +5 more
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TLDR
It is shown that MEMD overcomes 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.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.read more
Citations
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Multi-focus image fusion: A Survey of the state of the art
TL;DR: A comprehensive overview of existing multi-focus image fusion methods is presented and a new taxonomy is introduced to classify existing methods into four main categories: transformdomain methods, spatial domain methods, methods combining transform domain and spatial domain, and deep learning methods.
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From Multi-Scale Decomposition to Non-Multi-Scale Decomposition Methods: A Comprehensive Survey of Image Fusion Techniques and Its Applications
TL;DR: A comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail is demonstrated and would form basis for stimulating and nurturing advanced research ideas in image fusion.
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Fast Multivariate Empirical Mode Decomposition
TL;DR: A fast MEMD (FMEMD) algorithm is proposed and featured by the following contributions, which is consistent with EMD in terms of the dyadic filter bank property and is more effective in working at low sampling rate.
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Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications
TL;DR: It is shown that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.
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MRI and PET/SPECT image fusion at feature level using ant colony based segmentation
TL;DR: A new method of multimodal image fusion which makes use of a segmentation map given by the ant colony algorithm is proposed which improves the fusion results and provides images with more spatial and color information, when compared to state-of-the-art methods.
References
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The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
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TL;DR: A "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information is pursued and it is shown that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves.
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TL;DR: Two formulas are presented for judging the significance of the difference between correlated proportions and the chi square equivalent of one of the developed formulas.
On empirical mode decomposition and its algorithms
TL;DR: Empirical Mode Decomposition is presented, and issues related to its effective implementation are discussed, and an interpretation of the method in terms of adaptive constant-Q filter banks is supported.