Perfect reconstruction image modulation based on BEMD and quaternionic analytic signals

Qiao, Lihong; Niu, Kai-Fu; Wang, Ning; Peng, Lizhong

doi:10.1007/s11432-011-4330-8

Cited by 15 publications

(4 citation statements)

References 22 publications

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“…In addition, Nunes et al [32], [33] have Introduced EMD in image processing (twodimensional signal) and they have developed the algorithm for two-dimensional decomposition in empirical mode (BEMD). This algorithm has caught the attention of several researchers and has been applied in image denoising [34], image compression [35], [36], image segmentation [37], [38], scaling the image [39], extraction of image characteristics [40], texture synthesis [41], and classification of image texture [42]. There are many methods of decomposition signals and images (DCT, Wavelets, Fourier, etc.)…”

Section: Bi-dimensional Emperical Mode Decompositionmentioning

confidence: 99%

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

Elmoufidi

Skouta

Andaloussi

et al. 2022

Int. J. Image Grap.

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In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

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Section: Bi-dimensional Emperical Mode Decompositionmentioning

confidence: 99%

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

Elmoufidi

Skouta

Andaloussi

et al. 2022

Int. J. Image Grap.

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“…It decomposes the input signal into multiple scales, which represent different time-frequency components of the original signal [34]. Compared with the other time-frequency signal processing methods such as short-time Fourier transform and wavelet transform, the EMD method does not rely on any priori basic functions and leads to an adaptive decomposition process by its own characteristics of the data, which should be better at revealing signal features involved in the time-frequency localization behaviour [8][9][10][11]20]. As an adaptive signal denoise and analysis technology, the EMD has been applied in the study of earthquake, mechanical fault diagnosis, oceanography and other fields.…”

Section: Introductionmentioning

confidence: 99%

SAR image noise suppression of BEMD by the kernel principle component analysis

Huang

Zhou

et al. 2020

IET Image Processing

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In the process of synthetic aperture radar image noise suppression by the bi‐dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD‐kernel principal component analysis (BEMD‐KPCA) method of image denoising is proposed in this study. Experimental results show that the BEMDKPCA algorithm has a good capability of improving edge effects in the BEMD decomposition process and satisfying the requirement of the reliable decomposition results. Compared with the traditional BEMD method, the proposed approach has a good effect on suppressing speckle noise. Additionally, the denoised image from the decomposed components of the IMFs processed by the BEMD‐KPCA method sufficiently preserves the edge and detail information, confirming its high coherency with the original image.

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“…About ten years ago, Nunes et al [22,23] introduced the EMD into image processing and proposed the algorithm of bidimensional empirical mode decomposition (BEMD). Soon afterward, this algorithm received attention from some researchers and was put into image compression [24,25], image texture classification [26], image denoising [27], image texture segmentation [28,29], image scaling [30], texture synthesis [31], and image feature extraction [32]. Due to the noise characteristics of practical images, it is generally likely that the highest noise content of an image is extracted into the first mode component for the natural property of the BEMD process, and the remaining fewer noise contents sneak into the subsequent ones.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Image Denoising Performance of Bidimensional Empirical Mode Decomposition by Improving the Edge Effect

Lin

Zhou

et al. 2015

International Journal of Antennas and Propagation

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Bidimensional empirical mode decomposition (BEMD) algorithm, with high adaptive ability, provides a suitable tool for the noisy image processing, and, however, the edge effect involved in its operation gives rise to a problem—how to obtain reliable decomposition results to effectively remove noises from the image. Accordingly, we propose an approach to deal with the edge effect caused by BEMD in the decomposition of an image signal and then to enhance its denoising performance. This approach includes two steps, in which the first one is an extrapolation operation through the regression model constructed by the support vector machine (SVM) method with high generalization ability, based on the information of the original signal, and the second is an expansion by the closed-end mirror expansion technique with respect to the extrema nearest to and beyond the edge of the data resulting from the first operation. Applications to remove the Gaussian white noise, salt and pepper noise, and random noise from the noisy images show that the edge effect of the BEMD can be improved effectively by the proposed approach to meet requirement of the reliable decomposition results. They also illustrate a good denoising effect of the BEMD by improving the edge effect on the basis of the proposed approach. Additionally, the denoised image preserves information details sufficiently and also enlarges the peak signal-to-noise ratio.

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Perfect reconstruction image modulation based on BEMD and quaternionic analytic signals

Cited by 15 publications

References 22 publications

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

SAR image noise suppression of BEMD by the kernel principle component analysis

Enhancing Image Denoising Performance of Bidimensional Empirical Mode Decomposition by Improving the Edge Effect

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