Dorina Isar

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.

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.

New instantaneous frequency estimation method based on image processing techniques

The aim of this paper is to present a new method for the estimation of the instantaneous frequency of a frequency modulated signal, corrupted by additive noise. Any time-frequency representation of an acquired signal is concentrated around the instantaneous frequency law of its useful component (the projection of the ridges of the time-frequency representation on the time-frequency plane) and realizes the diffusion of its noise component. So, extracting the ridges of the time-frequency representation, the instantaneous frequency of its useful component can be estimated. In this paper a new time-frequency representation is proposed. Using the image of this new time-frequency representation, its ridges can be extracted with the aid of some mathematical morphology operators. This is a ridges detection mechanism producing the projection on the time-frequency plane. This projection represents the result of the proposed estimation method. Some simulations prove the qualities of this method.

A new method for denoising SONAR images

The SONAR images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. The use of speckle reduction filters is necessary to optimize the images exploitation procedures. This paper presents a new speckle reduction method in the wavelets domain using a novel Bayesian-based algorithm, which tends to reduce the speckle, preserving the structural features (like the discontinuities) and textural information of the scene. First, the different wavelet transforms are investigated and arguments to select the dual tree complex wavelet transform are presented. Next, accurate models for the subband decompositions of SONAR images, that permit the construction of maximum a posteriori filters, with closed-form input-output relations, are investigated. A blind speckle-suppression method that performs a nonlinear operation on the data is obtained. Finally, some simulation examples prove the performances of the proposed denoising method. These performances are compared with the results obtained applying state-of-the-art speckle reduction techniques.

Multi-scale MAP despeckling of sonar images

The sonar images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. The use of speckle reduction filters is necessary to optimize the images exploitation procedures. This paper presents a new speckle reduction method in the wavelets domain using a novel Bayesian-based algorithm, which tends to reduce the speckle, preserving the structural features (like the discontinuities) and textural information of the scene. A blind speckle-suppression method that performs a nonlinear operation on the data, based on a new bishrink filter variant is obtained. Finally, some simulation examples prove the performances of the proposed denoising method. These performances are compared with the results obtained applying state-of-the-art speckle reduction techniques.

A second order statistical analysis of the Hyperanalytic Wavelet Transform

We present a second order statistical analysis of the Hyperanalytic Wavelet Transform (HWT). The results are useful to design signal processing systems based on the wavelet theory.

Hyperanalytic wavelet packets

We introduce the hyperanalytic wavelet packets concept and we prove some of their properties: good frequency localization, quasi shift-invariance, quasi analyticity and quasi rotational invariance.

Image Denoising Using a Bishrink Filter with Reduced Sensitivity

The performance of image denoising algorithms using the Double Tree Complex Wavelet Transform, DT CWT, followed by a local adaptive bishrink filter can be improved by reducing the sensitivity of that filter with the local marginal variance of the wavelet coefficients. In this paper is proposed a solution for the sensitivity reduction based on enhanced diversity. First the advantages and disadvantages of a state-of-the-art denoising solution, based on the association DT CWT -bishrink filter are highlighted. Second a blind noise-suppression method correcting the disadvantages of the bishrink filter, performing a non-linear operation on the data is obtained. Finally, some simulation examples prove the performances of the proposed denoising method.

Adaptive De-Noising of Low SNR Signals.

In 1992 David Donoho has introduced the term denoising in connection with the adaptive nonlinear filtering in the wavelets transform domain. Despite its advantages this method isn’t used yet in the communications field. The goal of this paper is to improve this method and to apply it in communications. The improvements are a new threshold’s value searching method and the use of a new discrete wavelet transform. This new transform enhance the diversity in the wavelets transform domain. The results of a simulation prove the performances of the new denoising method.

A second order statistical analysis of the 2D Discrete Wavelet Transform

We present a general second order statistical analysis of the 2D Discrete Wavelet Transform (DWT) resulted after the computation of the correlation functions in all possible cases: inter-scale and inter-band dependency, inter-scale and intra-band dependency and intra-scale and intra-band dependency. The expected value and the variance of the wavelet coefficients are also computed. The resulting equations are useful for the design of different signal processing systems based on the wavelet theory.