SONAR Image Denoising Using a Bayesian Approach in the Wavelet Domain (original) (raw)
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SONAR images despeckling using a Bayesian approach in the wavelet domain
Proceedings of International Conference on Advances …, 2007
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.
A New Denoising System for SONAR Images
EURASIP Journal on …, 2009
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.
Denoising SONAR Images Using a Bishrink Filter with Reduced Sensitivity
The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. This paper presents a new denoising method in the wavelet domain, which tends to reduce the speckle, preserving the structural features (like the discontinuities) and textural information of the scene. Due to the massive proliferation of SONAR images, the proposed technique is very appealing in ocean applications. In fact it is a pre-treatment required in any SONAR images analysis system. In this paper we propose the adaptation to the case of speckle noise of a denoising method developed by the authors in the case of additive white Gaussian noise. It is simple and fast. Some simulation results and comparisons prove the performance of the new algorithm.
A new method for denoising SONAR images
Signals, Circuits and Systems, 2005. …, 2005
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 non-linear 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.
The SONAR images are perturbed by speckle noise. The despecklisation is necessary to optimize the image understanding. This paper presents, starting from a denoising method proposed by Donoho, a new filtering procedure, applied in the Double Tree Discrete Wavelet Transform domain. This technique reduces the noise without affecting the discontinuities. Some simulation examples prove the performances of the new denoising method.
2013
Under water environments are dynamic and complex, and obtaining a clear picture of the obstacles and movements of objects in this environment is difficult. The disturbances caused by various factors affect the image quality which leads to incorrect analysis. Sonar image quality can be assessed in terms of quality parameters like contrast, illumination variation and Noise. A non-parametric statistical wavelet denoising method is proposed in this paper. The proposed method incorporates the edge coefficients and non-edge coefficients as it picks up the homogeneous neighbor of non-edge coefficients and estimate the noise-free coefficients and outperforms the traditional approach.
Bayesian Hyperanalytic Denoising of SONAR Images
IEEE Geoscience and Remote Sensing Letters, 2000
The 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 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 Wavelet Transform (HWT) which is quasi shiftinvariant and has a good directional selectivity in association with a Maximum A Posteriori (MAP) filter named bishrink.
Image Denoising Using a Bishrink Filter with Reduced Sensitivity
2007
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.
Multi-scale MAP despeckling of sonar images
2005
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.
Image denoising is a key issue in all image processing researches. The great challenge of image denoising is how to preserve the edges and all fine details of an image when reducing the noise. In this paper, a comparative study of image denoising techniques for underwater SONAR (Sound Navigation and Ranging) images relying on spatial filters is presented. In particular four types of spatial filters (Average, Gaussian, Laplacian of Gaussian and Median filters) are applied to judge the efficiency. On each image, different window size configurations starting from 3x3 to 29x29 are applied and the performances of image filtering techniques are analyzed by the estimation of parametric values such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and execution time for filtering the images. It is observed that by increasing the window size the execution time will increase and PSNR values will decrease. With this analysis and from the results it is found that the optimum filter ...