One of the most challenging tasks for which EMD could be useful is that of non-parametric signal denoising, an area in which wavelet thresholding has been the dominant technique for many years. In this paper, the major wavelet thresholding principle is used in the decomposition modes resulting from applying EMD to a signal. We show, that although a direct application of this principle in the EM...

Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture ex...

This paper considers an improvement of empirical mode decomposition (EMD) in the presence of missing data. EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions. However, the conventional EMD may not be efficient when missing values are present. This paper proposes a modified EMD procedure ...

This paper describes a new technique, called the empirical mode decomposition (EMD), that allows the decomposition of one-dimensional signals into intrinsic oscillatory modes. The components, called intrinsic mode functions (IMFs), allow the calculation of a meaningful multicomponent instantaneous frequency. Applied to a seismic trace, the EMD allows us to study the di erent intrinsic oscillato...

Thresholding process is a fundamental image processing method. Typical thresholding methods are based on partitioning pixels in an image into two clusters. A new thresholding method is presented, in this paper. The main contribution of the proposed approach is the detection of an optimal image threshold exploiting the empirical mode decomposition (EMD) algorithm. The EMD algorithm can decompose...

This paper presents a new method based on empirical mode decomposition for enhancement of ECG (Electrocardiogram) signals. ECG signal has been widely used for diagnosis purposes of heart diseases. So a good quality ECG free from artifacts is required by physicians to easily and accurately diagnosis the physiological and pathological phenomena. However ECG recordings are often corrupted by artif...

Mastering the underlying characteristics of carbon price changes can help governments formulate correct policies to keep efficient operation of carbon markets, and investors take effective measures to evade their investment risks. Empirical mode decomposition (EMD), a self-adaption data analysis approach for nonlinear and non-stationary time series, can accurately explain the formation mechanis...

Electroencephalogram (EEG) is used to record electrical activity of brain. Human brain is fascinated by the different idea of thoughts and feelings generated from external and internal stimuli. Feature extraction and classification of EEG signal plays an important role in diagnosis of various brain diseases and mental tasks. In this paper, powerful technique of empirical mode decomposition (EMD...

Hilbert Huang Transform is a new developed method for signal processing especially suitable for non-stationary signal processing. In this paper, we propose a two dimensional Hilbert-Huang Transform based on Bidimensional Empirical Mode Decomposition (BEMD) and quaternionic analytic signal. Bidimensional Empirical Mode Decomposition is adaptive signal decomposition method and its decomposition r...

Image fusion is a process of combining relevant information from two or more images from different sensors based on certain algorithm. Many algorithms have been proposed for pixel level image fusion. In this paper, Empirical Mode Decomposition is the recent, powerful tool for adaptive multi scale analysis of non stationary signals that decomposes them into Intrinsic Mode Functions (IMFs). Hence...