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Non-Local or patch-based approaches are used in most of the state-of-the-art methods for image denoising. Denoising with regular square patches may cause noise halos around edges, particularly high contrasted edges. In order to overcome this drawback, this work presents an extension of the Non-Local TV framework that effectively exploits the potential local geometric features of the image by replacing the simple square patches with different oriented shapes. We first solve the denoised models of ROF-NLTV with different oriented patch shapes by Split Bregman algorithm. Then, use exponentially weighted aggregation method based on Stein’s unbiased risk estimate to combine the estimators obtained in the first step. The numerical results show our method outperforms some previous ones. Moreover, common noise halos around edges usually observed by denoising with Non-Local TV method are reduced thanks our approach.

A novel and efficient method for decomposing a signal into a set of intrinsic mode functions (IMFs) and a trend is proposed. Unlike the original empirical mode decomposition (EMD), which uses spline fits to extract variations from the signal by separating the local mean from the fluctuations in the decomposing process, this new method being proposed takes advantage of the theory of variable finite impulse response (FIR) filtering where filter coefficients and breakpoint frequencies can be adjusted to track any peak-to-peak time scale changes. The IMFs are results of a multiple variable frequency response FIR filtering when signals pass through the filters. Numerical examples validate that in contrast with the original EMD, the proposed method can fine-tune the frequency resolution and suppress the aliasing effectively.

Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals. The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio (SNR) variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron (MLP) classifier with better generalization, as well as, addition of time-frequency features set for classifying six difierent modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.

A novel and efficient approach for multicomponent chirp-like jammer excision in direct sequence spread spectrum (DSSS) communication systems using Hilbert-Huang Hough (HH-H) transform is proposed, which is the generalization of marginal Hilbert spectrum. The approach iteratively decomposes the received signal into intrinsic mode functions (IMFs) and applies Hilbert transform to each IMF to yield Hilbert spectrum (HS), by which the embedded jammers can be well concentrated in the time-frequency plane along their instantaneous frequency laws. By performing Hough transform over the HS and searching peaks in the parameter space, parameters estimation and components reconstruction of the multicomponent chirp-like jammer are implememted. Simulation results are presented showing performance comparison between the proposed method and the fractional Fourier transform (FrFT) based method in the case of two chirp-like jammer components. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2372

Applying compressed sensing (CS) to magnetic resonance imaging (MRI) makes it possible to reconstruct a MR image from undersampled data. Traditional CS based MR image reconstruction schemes only use the signals' sparsity in an appropriate transform domain to reduce sampling rate. This paper proposes a new MR image reconstruction method which utilizes structure features of the image besides sparsity. The proposed method exploits the distribution of the wavelet coefficients' magnitudes and support information to formulate the objective function by minimizing which the image is reconstructed. The objective function consists of the data consistence term and two regularization terms: l1-l2 norm of the groups which contains the coefficients with the unknown support and total variation (TV) norm of the image. The simulation results from real MR images show that the proposed method outperforms the conventional CS based MR image reconstruction method.