Noise-dominant Intrinsic Mode Functions Research Articles

A Novel Noise Reduction Method of UAV Magnetic Survey Data Based on CEEMDAN, Permutation Entropy, Correlation Coefficient and Wavelet Threshold Denoising

Despite the increased attention that has been given to the unmanned aerial vehicle (UAV)-based magnetic survey systems in the past decade, the processing of UAV magnetic data is still a tough task. In this paper, we propose a novel noise reduction method of UAV magnetic data based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE), correlation coefficient and wavelet threshold denoising. The original signal is first decomposed into several intrinsic mode functions (IMFs) by CEEMDAN, and the PE of each IMF is calculated. Second, IMFs are divided into four categories according to the quartiles of PE, namely, noise IMFs, noise-dominant IMFs, signal-dominant IMFs, and signal IMFs. Then the noise IMFs are removed, and correlation coefficients are used to identify the real signal-dominant IMFs. Finally, the wavelet threshold denoising is applied to the real signal-dominant IMFs, the denoised signal can be obtained by combining the signal IMFs and the denoised IMFs. Both synthetic and field experiments are conducted to verify the effectiveness of the proposed method. The results show that the proposed method can eliminate the interference to a great extent, which lays a foundation for the further interpretation of UAV magnetic data.

A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry

Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy.

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

An improved two-dimensional variational mode decomposition algorithm and its application in oil pipeline image

In this paper, an image denoising algorithm is presented based on the two-dimensional variational mode decomposition (2D-VMD) and the Hausdorff distance (HD). The procedure of the developed algorithm is that: (1) use the 2D-VMD to decompose the image into a number of intrinsic mode functions (IMFs); (2) use the HD and the probability density function (PDF) to distinguish the signal-dominant IMFs (S-D IMFs) and the noise-dominant IMFs (N-D IMFs), and then use the wavelet threshold denoising method to eliminate the noise in the N-D IMFs; and (3) use the denoised IMFs and the S-D IMFs to reconstruct the image and obtain the denoised image. The effectiveness of the proposed algorithm is verified on the oil pipeline images. Simulation results show that, compared with the median filtering, the wavelet threshold filtering, and the Rudin Osher Fatemi, the proposed algorithm has a better denoising effect in both subjective and objective evaluation.

Image denoising based on improved bidimensional empirical mode decomposition thresholding technology

In this paper, a novel image denoising methodology based on improved bidimensional empirical mode decomposition and soft interval thresholding technique is proposed. First, a noise compressed image is constructed. Then, the noise compressed image is decomposed by means of bidimensional empirical mode decomposition (BEMD) into a series of intrinsic mode functions (IMFs), which are separated into signal-dominant IMFs and noise-dominant IMFs using a similarity measure based on l2-norm and a probability density function, and a soft interval thresholding technique is used adaptively to remove the noise inherent in noise-dominant IMFs. Finally, a denoised image is reconstructed by combining the signal-dominant IMFs and the denoised noise-dominant IMFs. The performance of the proposed denoising method is evaluated by using multiple images with different types of noise, and results from the proposed method are compared with those of other conventional methods in various noisy environments. Simulation results demonstrate that the proposed denoising method outperforms other denoising methods in terms of peak signal-to-noise ratio, mean square error and energy of the first IMF.

Different Scenarios on Denoising of Signals in the Intrinsic Mode Function Selection Framework

ABSTRACTWithout having any information of original signal, estimating the desired signal from noisy measurements is a challenging problem. In this paper, the denoising problem of signals corrupted by additive white Gaussian noise (AWGN) is considered in the empirical mode decomposition (EMD) framework, and five different noise suppression scenarios based on the various combinations of intrinsic mode functions (IMFs) that arise from applying the EMD to a given noisy signal are suggested. In these scenarios, the idea of discarding noise-dominant IMFs from a noisy signal is adopted. Considering the root-mean-square error and the signal-to-error ratio, the performance of each scenario is evaluated over simulated and real signals contaminated by AWGN with different signal-to-noise ratios (SNRs). It is observed from simulations that the proposed scenarios provide satisfactory denoising performance especially for positive SNRs and can be exploited as a primary stage in whole of the noise-diminishing applications.