Hybrid image fusion scheme using self-fractional Fourier functions and multivariate empirical mode decomposition

Sharma, Janki Ballabh; Sharma, Kamalesh Kumar; Sahula, Vineet

doi:10.1016/j.sigpro.2014.01.001

Cited by 26 publications

(5 citation statements)

References 25 publications

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“…Additionally, a comparison is performed between the suggested method and different well-known methods, including DCT (38), DWT (39), FFT (40), and HIS (41). As well, the proposed optimization is compared with some famous optimizers like Genetic Algorithm and Particle Swarm Optimization.…”

Section: Standard Deviationmentioning

confidence: 99%

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Jiang

2020

Ann Transl Med

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Background: There are various applications for medical image fusion schemes in different medical clinics.Here, the generalized version of the homomorphic filtering technique involving the Fourier domain for image and signal processing is a proper method. Methods:The methods on the wavelet transform proposes some advantages in the discretization of multimodality medical images fusion, conducted in the Fourier spectrum. In the present study, an optimal version of the homomorphic fusion, namely optimum homomorphic wavelet fusion (OHWF) on the hybrid particle swarm and ant colony optimization methods, is presented. The presented OHWF, including some domains including wavelet and logarithmic, and besides, the wavelet allows the OHWF technique to decompose the images in the multi-level process.Results: In this work, the modality one approximation coefficients and the coefficients belong to modality two are presented in adder1. While in the case of adder two, the modality one optimal scaled detailed constant values of modality one and the approximation coefficients refer to modality two are added in conjunction. The pixel-based averaged principle is applied to fuse the address one and two results simultaneously. First, the intended fusion technique is authenticated, applying different fusion assessment metrics for MR-PET, MR-SPECT, MR T1-T2, and MR-CT image fusions. And then, the proposed hybrid particle swarm optimizer (PSO) and ACO algorithms applied to obtain the best image fusion. Conclusions:The empirical data illustrates that the presented method performs a desiring ability in image fusion in the case of functional and structural data.

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Section: Standard Deviationmentioning

confidence: 99%

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Jiang

2020

Ann Transl Med

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“…However, they are not suited for the signal of complex construction. A self-adaptive method for non-stationary signals and nonlinear is called Empirical mode decomposition (EMD) [19], and it has been successfully implemented for; (a) fault diagnosis [20], (b) wind energy [21], (c) flight flutter [22], (d) image processing [23], (e) health monitoring [24], (f) electroencephalogram (EEG) analysis [25], and (g) electrocardiogram (ECG) signals [26]. Moreover, it still flops to disintegrate a signal within the existence of a high trend.…”

Section: Introductionmentioning

confidence: 99%

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

Algburi

Gao

2019

Energies

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In an industrial robot, rotary encoders have been extensively used for dynamic control and positioning. This study shows that the encoder signal, after appropriate processing, can also be efficiently utilized for the health observation of energy performance of industrial robots system. Singular spectrum analysis (SSA) and Hilbert transform (HT) is proposed in this work, for detecting weak position oscillations to estimate the instantaneous amplitudes (IA) and the instantaneous frequencies (IF) of an industrial robot based on the encoder signal. Compared with empirical mode decomposition (EMD) and HT, the singular spectrum analysis and Hilbert transform (SSAHT) outperforms empirical mode decomposition Hilbert transform (EMDHT) in terms of ability and precision to determine source noise, and it can accurately catch the weak oscillations without signal deformation in both position and speed introduced via mechanical flaws. Combined with SSA, the IA and IF of both oscillations and residual are extracted by HT. They are obtained from the robot arm movement. These features play an important role in improving the performance detecting weak oscillations and the residual, essential information to evaluate the health conditions and fault detection to serve the energy performance for the industrial robot. The efficiency of the proposed system has been verified both numerical simulation and experimental data. The outcomes prove that the proposed SSAHT can detect flaw indications and additionally, it can also identify faulty components. Thus, the study presents a promising tool for the health monitoring of an industrial robot instead of the vibration-based monitoring scheme.

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“…And Zhou et al presented a novel multi-scale weighted gradient-based fusion (MWGF) method to solve image fuzzy, which can reflect the sharpness of image edges and corner structures at multiple scales [30]. In the frequency domain of image, Sharma et al proposed a hybrid image fusion method combining self-fractional Fourier function (SFFF) decomposition and multivariate empirical mode decomposition [31]. Yang et al presented a novel multi-wavelength image fusion method based onnonsubsampled contourlet transform (NSCT), which fused the coefficients of low frequency and high frequency sub-bands of image respectively by sum-modified-Laplacian and local Log-Gabor energy [32].…”

Section: Introductionmentioning

confidence: 99%

A Fusion Method in Frequency Domain for Multi-Wavelength Transmission Image

Liu

Yan

et al. 2019

IEEE Access

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Multi-wavelength transmission imaging provides a possibility for early breast cancer detection. However, due to the strong scattering effect of light source in the transmission process of biological tissue, there exist many difficulties in the heterogeneous identification of multi-wavelength images. In this paper, a multi-wavelength images frequency fusion method based on regional variance significance and regional space energy weighting is proposed to improve the accuracy of heterogeneous detection. Firstly, the acquisition experiment of phantom multi-wavelength transmission images is designed. And R\G\B threechannel wavelength images are extracted from multi-wavelength images as an example to realize subsequent image fusion. Secondly, frame accumulation technique and second-order Butterworth filter are used to improve the signal-to-noise ratio of images and suppress the noise of images. Then, after homomorphic filtering, it is found that the same frequency domain of images is connected, which is not conducive to the recognition of heterogeneity in image. In order to break the connectivity of images in the same frequency domain and improve the accuracy of heterogeneous detection, the regional variance significance and the regional space energy weighting fusion strategies are used to reconstruct the low-frequency and high-frequency fusion images respectively. Finally, the heterogeneous detection results of the proposed image fusion method and the state-of-the-art methods in the multi-wavelength image are compared. The experimental results show that compared with the state-of-the-art image fusion methods, the fusion method proposed in this paper can identify the location of heterogeneous region more accurately and significantly improve the contrast between heterogeneous region and normal region in the fusion image. In conclusion, the fusion algorithm not only effectively realizes the recognition of heterogeneity in multi-wavelength images, but also promotes the potential application of multi-wavelength transmission image in early breast cancer detection. INDEX TERMS Multi-wavelength transmission image, fusion method, regional variance significance (RVS), regional space energy weighting (RSEW), heterogeneous region.

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Hybrid image fusion scheme using self-fractional Fourier functions and multivariate empirical mode decomposition

Cited by 26 publications

References 25 publications

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

A Fusion Method in Frequency Domain for Multi-Wavelength Transmission Image

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