Faten Ben Arfia - Academia.edu (original) (raw)
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Papers by Faten Ben Arfia
Proceedings of SPIE, Apr 15, 2011
In this paper we develop an adaptive algorithm for decomposition of greyscales images. This metho... more In this paper we develop an adaptive algorithm for decomposition of greyscales images. This method is highly adaptive decomposition image called Bidimentional Empirical Mode Decomposition (BEMD). It is based on the characterization of the image through its decomposition in Intrinsic Mode Function (IMF) where it can be decomposed into basis functions called IMF and a residue. This method offered a
This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the... more This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise.
This paper proposes a method for image denoising in the filter domain based on the characteristic... more This paper proposes a method for image denoising in the filter domain based on the characteristics of the Empirical Mode Decomposition (EMD) and the wavelet technique. The proposed method uses the EMD to the decomposition and double density wavelet to filter components. Our experimental results show that these image denoising methods are more efficient than the wavelet denoising method. Finally, the PSNR (peak signal noise ratio) and the visualization of the denoising image are used as performance comparison indexes.
In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales imag... more In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales images. This method is highly adaptive decomposition image called Bidimensional Empirical Mode Decomposition (BEMD) based in blocks. This proposed approach decomposes image into a basis functions named Intrinsic Mode Function (IMF) and residue. This method offers a good result in visual quality but it consumes an important execution time. To overcome this problem we propose a new approach using Block based BEMD method where the input image is subdivided into blocks. Then the conventional BEMD is applied on each of the four blocks separately. This proposed extended method gives a solution in reduction of execution time. This approach shows the good results in the field of image filtering. Denoised image is obtained by summing the residue and the filtered first IMFs (the detail) using a wavelet technique. Experimental results positively show that this proposed methodology removes Gaussian and ...
International Journal of Computer Applications, 2012
In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales imag... more In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales images. This method is highly adaptive decomposition image called Bidimensional Empirical Mode Decomposition (BEMD) based in blocks. This proposed approach decomposes image into a basis functions named Intrinsic Mode Function (IMF) and residue. This method offers a good result in visual quality but it consumes an important execution time. To overcome this problem we propose a new approach using Block based BEMD method where the input image is subdivided into blocks. Then the conventional BEMD is applied on each of the four blocks separately. This proposed extended method gives a solution in reduction of execution time. This approach shows the good results in the field of image filtering. Denoised image is obtained by summing the residue and the filtered first IMFs (the detail) using a wavelet technique. Experimental results positively show that this proposed methodology removes Gaussian and Impulsive noises from the images.
Third International Conference on Digital Image Processing (ICDIP 2011), 2011
In this paper we develop an adaptive algorithm for decomposition of greyscales images. This metho... more In this paper we develop an adaptive algorithm for decomposition of greyscales images. This method is highly adaptive decomposition image called Bidimentional Empirical Mode Decomposition (BEMD). It is based on the characterization of the image through its decomposition in Intrinsic Mode Function (IMF) where it can be decomposed into basis functions called IMF and a residue. This method offered a
2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
ABSTRACT Thresholding is one of the popular and fundamental techniques for conducting image segme... more ABSTRACT Thresholding is one of the popular and fundamental techniques for conducting image segmentation. It is a widely used tool in image segmentation for extracting the object regions from their background. In this paper, image segmentation method based on two-dimensional histogram analysis through entropy maximization is presented. The 2-D maximum entropy threshold approach is proposed to segment a gray-scale image. To compensate for the weakness of the classical methods that may be trapped into the first entropy local maximum met, a new heuristic optimization algorithm, called the particle swarm optimization PSO is introduced. PSO algorithm is realized successfully in the process of solving the 2-D maximum entropy problem. Therefore, the convergence is improved and the reproducibility of the optimal solutions is better guaranteed. The experiments of segmenting images are illustrated to show that the proposed method can get ideal segmentation result.
2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
ABSTRACT The data interpolation is an essential part of Bidimensional Empirical Mode Decompositio... more ABSTRACT The data interpolation is an essential part of Bidimensional Empirical Mode Decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and lower envelopes, respectively. Because of the properties of radial basis function (RBF) interpolators, they are good candidates for use in BEMD. However, only one or two of the RBF interpolators have been utilized for BEMD so far. This paper employs many RBF interpolators for BEMD, compares their performances, and finds out the useful ones for BEMD especially in the image filtering application. We propose to apply the BEMD approach with the adequate interpolation function in the image denoising domain. After that, we combine the BEMD with the DWT to improve the BEMD denoising method. The analysis is done using real images. Simulations are made to focus mainly on the effect of interpolation methods by providing less or negligible control on the other parameters of the BEMD process. The study is believed to work as a guideline in the area of BEMD based real image in the filtering application.
… Conference on Systems, …, 2010
This paper proposes a method for image denoising in the filter domain based on the characteristic... more This paper proposes a method for image denoising in the filter domain based on the characteristics of the Empirical Mode Decomposition (EMD) and the wavelet technique. The proposed method uses the EMD to the decomposition and double density wavelet to filter components. Our experimental results show that these image denoising methods are more efficient than the wavelet denoising method. Finally, the PSNR (peak signal noise ratio) and the visualization of the denoising image are used as performance comparison indexes.
Leonardo Journal of Sciences, 2009
This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the... more This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm ...
This paper presents two proposed approaches to color image decomposition with Bidimensional Empir... more This paper presents two proposed approaches to color image decomposition with Bidimensional Empirical Mode Decomposition (BEMD) technique. The first one applies the BEMD on each channel separately and the second is based on interpolation of each channel in the sifting process. The application of the two methods shows the same performance of each approach in terms of PSNR and visual quality, but they do not provide the same results in execution time which presents the most important criterion in real time applications. It was shown that the second BEMD approach based on interpolation of each channel in the sifting process, gives a gain in the point of view the execution time.
2011 17th International Conference on Digital Signal Processing (DSP), 2011
This paper presents a new adaptive approach for image denoising with Gaussian noise based on a co... more This paper presents a new adaptive approach for image denoising with Gaussian noise based on a combination of the Bidimensional Empirical Mode Decomposition (BEMD) and the the discrete wavelet transforms (DWT). The BEMD is an auto-adaptive method for the analysis of nonlinear or non-stationary signals and images. The input image is decomposed into several modes called Intrinsic Mode Functions (IMFs),
Proceedings of SPIE, Apr 15, 2011
In this paper we develop an adaptive algorithm for decomposition of greyscales images. This metho... more In this paper we develop an adaptive algorithm for decomposition of greyscales images. This method is highly adaptive decomposition image called Bidimentional Empirical Mode Decomposition (BEMD). It is based on the characterization of the image through its decomposition in Intrinsic Mode Function (IMF) where it can be decomposed into basis functions called IMF and a residue. This method offered a
This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the... more This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise.
This paper proposes a method for image denoising in the filter domain based on the characteristic... more This paper proposes a method for image denoising in the filter domain based on the characteristics of the Empirical Mode Decomposition (EMD) and the wavelet technique. The proposed method uses the EMD to the decomposition and double density wavelet to filter components. Our experimental results show that these image denoising methods are more efficient than the wavelet denoising method. Finally, the PSNR (peak signal noise ratio) and the visualization of the denoising image are used as performance comparison indexes.
In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales imag... more In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales images. This method is highly adaptive decomposition image called Bidimensional Empirical Mode Decomposition (BEMD) based in blocks. This proposed approach decomposes image into a basis functions named Intrinsic Mode Function (IMF) and residue. This method offers a good result in visual quality but it consumes an important execution time. To overcome this problem we propose a new approach using Block based BEMD method where the input image is subdivided into blocks. Then the conventional BEMD is applied on each of the four blocks separately. This proposed extended method gives a solution in reduction of execution time. This approach shows the good results in the field of image filtering. Denoised image is obtained by summing the residue and the filtered first IMFs (the detail) using a wavelet technique. Experimental results positively show that this proposed methodology removes Gaussian and ...
International Journal of Computer Applications, 2012
In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales imag... more In this paper we develop an adaptive algorithm for decomposition and filtering of grayscales images. This method is highly adaptive decomposition image called Bidimensional Empirical Mode Decomposition (BEMD) based in blocks. This proposed approach decomposes image into a basis functions named Intrinsic Mode Function (IMF) and residue. This method offers a good result in visual quality but it consumes an important execution time. To overcome this problem we propose a new approach using Block based BEMD method where the input image is subdivided into blocks. Then the conventional BEMD is applied on each of the four blocks separately. This proposed extended method gives a solution in reduction of execution time. This approach shows the good results in the field of image filtering. Denoised image is obtained by summing the residue and the filtered first IMFs (the detail) using a wavelet technique. Experimental results positively show that this proposed methodology removes Gaussian and Impulsive noises from the images.
Third International Conference on Digital Image Processing (ICDIP 2011), 2011
In this paper we develop an adaptive algorithm for decomposition of greyscales images. This metho... more In this paper we develop an adaptive algorithm for decomposition of greyscales images. This method is highly adaptive decomposition image called Bidimentional Empirical Mode Decomposition (BEMD). It is based on the characterization of the image through its decomposition in Intrinsic Mode Function (IMF) where it can be decomposed into basis functions called IMF and a residue. This method offered a
2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
ABSTRACT Thresholding is one of the popular and fundamental techniques for conducting image segme... more ABSTRACT Thresholding is one of the popular and fundamental techniques for conducting image segmentation. It is a widely used tool in image segmentation for extracting the object regions from their background. In this paper, image segmentation method based on two-dimensional histogram analysis through entropy maximization is presented. The 2-D maximum entropy threshold approach is proposed to segment a gray-scale image. To compensate for the weakness of the classical methods that may be trapped into the first entropy local maximum met, a new heuristic optimization algorithm, called the particle swarm optimization PSO is introduced. PSO algorithm is realized successfully in the process of solving the 2-D maximum entropy problem. Therefore, the convergence is improved and the reproducibility of the optimal solutions is better guaranteed. The experiments of segmenting images are illustrated to show that the proposed method can get ideal segmentation result.
2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2014
ABSTRACT The data interpolation is an essential part of Bidimensional Empirical Mode Decompositio... more ABSTRACT The data interpolation is an essential part of Bidimensional Empirical Mode Decomposition (BEMD) of an image. In the decomposition process, local maxima and minima of the image are extracted at each iteration and then interpolated to form the upper and lower envelopes, respectively. Because of the properties of radial basis function (RBF) interpolators, they are good candidates for use in BEMD. However, only one or two of the RBF interpolators have been utilized for BEMD so far. This paper employs many RBF interpolators for BEMD, compares their performances, and finds out the useful ones for BEMD especially in the image filtering application. We propose to apply the BEMD approach with the adequate interpolation function in the image denoising domain. After that, we combine the BEMD with the DWT to improve the BEMD denoising method. The analysis is done using real images. Simulations are made to focus mainly on the effect of interpolation methods by providing less or negligible control on the other parameters of the BEMD process. The study is believed to work as a guideline in the area of BEMD based real image in the filtering application.
… Conference on Systems, …, 2010
This paper proposes a method for image denoising in the filter domain based on the characteristic... more This paper proposes a method for image denoising in the filter domain based on the characteristics of the Empirical Mode Decomposition (EMD) and the wavelet technique. The proposed method uses the EMD to the decomposition and double density wavelet to filter components. Our experimental results show that these image denoising methods are more efficient than the wavelet denoising method. Finally, the PSNR (peak signal noise ratio) and the visualization of the denoising image are used as performance comparison indexes.
Leonardo Journal of Sciences, 2009
This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the... more This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm ...
This paper presents two proposed approaches to color image decomposition with Bidimensional Empir... more This paper presents two proposed approaches to color image decomposition with Bidimensional Empirical Mode Decomposition (BEMD) technique. The first one applies the BEMD on each channel separately and the second is based on interpolation of each channel in the sifting process. The application of the two methods shows the same performance of each approach in terms of PSNR and visual quality, but they do not provide the same results in execution time which presents the most important criterion in real time applications. It was shown that the second BEMD approach based on interpolation of each channel in the sifting process, gives a gain in the point of view the execution time.
2011 17th International Conference on Digital Signal Processing (DSP), 2011
This paper presents a new adaptive approach for image denoising with Gaussian noise based on a co... more This paper presents a new adaptive approach for image denoising with Gaussian noise based on a combination of the Bidimensional Empirical Mode Decomposition (BEMD) and the the discrete wavelet transforms (DWT). The BEMD is an auto-adaptive method for the analysis of nonlinear or non-stationary signals and images. The input image is decomposed into several modes called Intrinsic Mode Functions (IMFs),