Bayesian Hyperanalytic Denoising of SONAR Images

Firoiu, Ioana; Naforniţa, Corina; Isar, Dorina; Isar, Alexandru

doi:10.1109/lgrs.2011.2155617

Cited by 23 publications

(6 citation statements)

References 13 publications

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“…Further objective performance measures could be found in collaboration with geologists. Another future research direction consists in the association of the proposed inpainting method with a denoising method, for example the method proposed in [9]. Such an association could finalize the pretreatment phase from a signal processing chain dedicated to SONAR images.…”

Section: Discussionmentioning

confidence: 99%

Impainting SONAR images by exemplar-based algorithm

Stolojescu-Crisan

Isar

2013

International Symposium on Signals, Circuits and Systems ISSCS2013

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An essential problem in image processing is the recovery of lost or damaged data, which can be done by inpainting. In the recent years, a large variety of image inpainting algorithms have been proposed in the literature. This paper presents a comparison between two exemplar based image inpainting algorithms, in the case of SONAR images. A number of simulations on real SONAR images prove the effectiveness of these algorithms in reconstructing the affected parts of the images. The performance is analyzed in terms of the visual quality and comparing the Peak Signal to Noise Ratio.

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Section: Discussionmentioning

confidence: 99%

Impainting SONAR images by exemplar-based algorithm

Stolojescu-Crisan

Isar

2013

International Symposium on Signals, Circuits and Systems ISSCS2013

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“…This model is used in cases of Synthetic Aperture Radar (SAR) images [31][32][33] or SONAR images [34,35]. The statistical model of the speckle noise is presented in Appendix A.…”

Section: Multiplicative Noisementioning

confidence: 99%

“…We subtract this mean from the denoising result and we add the mean of the acquired image computed by the first mean computation block. Taking into account the considerations about the sensitivity of the bishrink filter from the end of the previous section, a block for sensitivity reduction could be included in the denoising system [34,35].…”

Section: Multiplicative Noisementioning

confidence: 99%

Hyperanalytic Wavelet-Based Robust Edge Detection

2021

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The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

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“…This drawback can be diminished using diversification techniques and non-local mean (NLM) algorithms for the pixels with small nlv [26]. In [27] the bishrink filter was associated with HWT. This denoising method was considered for the simulations part of this paper.…”

Section: Image Denoisingmentioning

confidence: 99%

Space-Frequency Localization as Bivariate Mother Wavelets Selecting Criterion for Hyperanalytic Bayesian Image Denoising

Isar

2012

Fluct. Noise Lett.

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Communicated by Stefania ResidoriThroughout recent years, many wavelet transforms (WTs) were used in digital image processing: the discrete WT (DWT), the stationary WT (SWT) or the hyperanalytic WT (HWT). All these transforms have in common a feature, the mother wavelets (MW). A great number of MWs was already proposed in literature. The purpose of this paper is the selection of MW for hyperanalytic Bayesian image denoising on the basis of its space-frequency localization. The MW with the same space-frequency localization as the elements of the input image gives the better results. Some procedures for the evaluation of the space-frequency localization of MWs and input images are proposed and applied to optimize the results obtained by the simulations of denoising, indicating the most appropriate MW. Keywords: Mother wavelets; space-frequency; denoising. 1250009-1 Fluct. Noise Lett. 2012.11. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 02/06/15. For personal use only. A. IsariH{ψ} and ψ a = ψ + iH{ψ} are also MWs. H denotes the Hilbert transform operator. This wavelets pair (ψ, iH{ψ}) defines a complex discrete wavelet transform (CDWT). A complex wavelet coefficient is obtained by interpreting the wavelet coefficient from one DWT tree as being its real part, whereas the corresponding coefficient from the other tree is considered its imaginary part. In [4], the DTCWT, which is a quadrature pair of DWT trees similar to the CDWT, is developed. The DTCWT coefficients may be interpreted as arising from the DWT associated with a quasi-analytic wavelet. Both DTCWT and CDWT are invertible and quasi shiftinvariant. The implementation of analytic wavelet transform (AWT) is presented in [6]. First, a Hilbert transform is applied to the data. The real wavelet transform is then applied to the analytical signal associated to the input data, obtaining complex coefficients. The DTCWT and the AWT are equivalent because: d DTCWT [m, n] = x(t), ψ m,n (t) + iH{ψ m,n (t)} = x(t), ψ m,n (t) − i x(t), H{ψ m,n (t)} = x(t), ψ m,n (t) + i H{x(t)}, ψ m,n (t) = x(t) + iH{x(t)}, ψ m,n (t) = d AWT [m, n]

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Bayesian Hyperanalytic Denoising of SONAR Images

Cited by 23 publications

References 13 publications

Impainting SONAR images by exemplar-based algorithm

Impainting SONAR images by exemplar-based algorithm

Hyperanalytic Wavelet-Based Robust Edge Detection

Space-Frequency Localization as Bivariate Mother Wavelets Selecting Criterion for Hyperanalytic Bayesian Image Denoising

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