Alexandru Isar

Image Denoising Using a New Implementation of the Hyperanalytic Wavelet Transform

Shift invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, e.g., the double-tree complex WT (DTCWT), have these useful properties. In this paper, we propose the use of a recently introduced implementation of such a WT, namely, the hyperanalytic WT (HWT), in association with filtering techniques already used with the discrete WT (DWT). The result is a very simple and fast image denoising algorithm. Some simulation results and comparisons prove the performance obtained using the new method.

Bayesian Hyperanalytic Denoising of SONAR Images

The SOund Navigation And Ranging (SONAR) images are perturbed by speckle noise. This paper presents a new denoising method in the wavelet domain, which tends to reduce the speckle, preserving the structural features and the textural information of the scene. Shift invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in denoising of SONAR images. In this paper, we propose the use of a variant of hyperanalytic WT, which is quasi-shift invariant and has good directional selectivity in association with a maximum a posteriori filter named bishrink. This filter makes a very good treatment of the contours. The corresponding denoising algorithm is simple and fast. Its performance was proved on images perturbed by synthesized speckle noise and on real SONAR images.

ECG statistical denoising in the wavelet domain

The paper presents a denoising algorithm particularly suited to ECG signals processing. The main stage of this algorithm consists in a MAP filtering in the wavelet domain. Its effectiveness relies on the qualities of the wavelet transform and of the statistical filter used. Tests made on ECG signals, in realistic conditions, showed very promising results. The noise is almost completely removed, while the useful waveforms are preserved.

On the time-frequency localization of the wavelet signals, with application to orthogonal modulations

Orthogonal modulations are powerful techniques for digital data transmission, very well suited to radio channels. They use orthogonal basis, whose elements will be the “subcarriers” used in transmission. Some advantages of the wavelet functions compared to the windowed complex exponentials used in Orthogonal Frequency Division Multiplexing (OFDM) are highlighted. Thus, theoretical proof that Daubechies wavelets have better time-frequency localization is provided, opening new paths towards modern orthogonal modulation approaches, based on wavelets.

A New Implementation of the Hyperanalytic Wavelet Transform

The property of shift-invariance associated with a good directional selectivity are important for the application of a wavelet transform in many fields of image processing. Unfortunately, the 2D discrete wavelet transform is shift-variant and has a reduced directional selectivity. These disadvantages can be attenuated if a complex wavelet transform is used. In this paper, we propose a new implementation of such a wavelet transform, recently introduced, the hyper analytic wavelet transform. It is quasi shift-invariant, it has a good directional selectivity, and a reduced degree of redundancy of 4 in 2D. The implementation proposed is very simple. The properties already mentioned are proved by simulation.

Regularised, semi-local hurst estimation via generalised lasso and dual-tree complex wavelets

Semi-local Hurst estimation is considered for random fields where the regularity varies in a piecewise manner. The recently developed generalised lasso is exploited to propose a spatially regularised Hurst estimator. Dual-tree complex wavelets are used to formulate the usual log-spectrum regression problem and an interlaced penalty matrix is constructed to form a 2-d fused lasso constraint on the double-indexed parameters. We thus extend a regularity-based denoising approach and demonstrate the utility of our method with experiments.

Electrocardiogram baseline wander removal using stationary wavelet approximations

In this paper, a method to reduce the baseline wandering of an electrocardiogram signal is presented. The described method is based on stationary wavelet approximation of the whole signal. The main advantage of this method, compared with others, is the fact that it is a nonsupervised method, allowing the process to be used in an automatic analysis of electrocardiograms. Moreover, the results are as accurate as those obtained with other methods of baseline wandering removal, methods considered as references in the scientific bibliography.

A new denoising system for SONAR images

The SONAR images are perturbed by speckle noise. The use of speckle reduction filters is necessary to optimize the image exploitation procedures. This paper presents a new denoising method in the wavelet domain, which tends to reduce the speckle, preserving the structural features and textural information of the scene. Shift-invariance associated with good directional selectivity is important for the use of a wavelet transform (WT) in many fields of image processing. Generally, complex wavelet transforms, for example, the Double Tree Complex Wavelet Transform (DT-CWT) have these useful properties. In this paper, we propose the use of the DT-CWT in association with Maximum A Posteriori (MAP) filters. Such systems carry out different quality denoising in regions with different homogeneity degree. We propose a solution for the reduction of this unwanted effect based on diversity enhancement. The corresponding denoising algorithm is simple and fast. Some simulation results prove the performance obtained.

The reduction of interference terms in the time-frequency plane

There are few techniques for the reduction of interference terms. The most important between them is the reallocation technique. Other methods are based on the decomposition in frames of the signals to be processed. These frames represent discrete variants of some corresponding linear time-frequency representations. The aim of this paper is to propose a new method for the Wigner-Ville time-frequency representations interference term reduction. This method is based on the conjoint use of the Gabor and Wigner-Ville time-frequency representations of the same signal. Simulations prove the performances of the new method.

A physical layer simulator for WiMAX in Rayleigh fading channel

WiMAX is a wireless technology which offers high data rate transmission in broadband. In this paper, the architecture of the WiMAX physical layer simulator is presented. The main blocks are implemented with the aid of the Matlab programming language and the bit error rate (BER) curves are presented in Rayleigh fading channel.