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El Hanouti Imad El Fadili Hakim Zenkouar Khalid 《Multimedia Tools and Applications》2021,80(9):13801-13820
Multimedia Tools and Applications - Recently, a new image encryption-scheme for embedded systems based on continuous third-order hyperbolic sine chaotic system, has been proposed. The... 相似文献
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Multimedia Tools and Applications - A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10592-x 相似文献
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In this paper, we first present a new implementation of the 3-D fast curvelet transform, which is nearly 2.5 less redundant
than the Curvelab (wrapping-based) implementation as originally proposed in Ying et al. (Proceedings of wavelets XI conference,
San Diego, 2005) and Candès et al. (SIAM Multiscale Model. Simul. 5(3):861–899, 2006), which makes it more suitable to applications including massive data sets. We report an extensive comparison in denoising
with the Curvelab implementation as well as other 3-D multi-scale transforms with and without directional selectivity. The
proposed implementation proves to be a very good compromise between redundancy, rapidity and performance. Secondly, we exemplify
its usefulness on a variety of applications including denoising, inpainting, video de-interlacing and sparse component separation.
The obtained results are good with very simple algorithms and virtually no parameter to tune. 相似文献
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François Lecellier Jalal Fadili Stéphanie Jehan-Besson Gilles Aubert Marinette Revenu Eric Saloux 《Journal of Mathematical Imaging and Vision》2010,36(1):28-45
In this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random
variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special
Gaussian case. In the framework developed in this paper, we consider the general case of region-based terms involving functions
of parametric probability densities, for which the anti-log-likelihood function is a special case. Using shape derivative
tools, our effort focuses on constructing a general expression for the derivative of the energy (with respect to a domain),
and on deriving the corresponding evolution speed. More precisely, we first show by an example that the estimator of the distribution
parameters is crucial for the derived speed expression. On the one hand, when using the maximum likelihood (ML) estimator
for these parameters, the evolution speed has a closed-form expression that depends simply on the probability density function.
On the other hand, complicating additive terms appear when using other estimators, e.g. method of moments. We then proceed
by stating a general result within the framework of multi-parameter exponential family. This result is specialized to the
case of the anti-log-likelihood function with the ML estimator and to the case of the relative entropy. Experimental results
on simulated data confirm our expectations that using the appropriate noise model leads to the best segmentation performance.
We also report preliminary experiments on real life Synthetic Aperture Radar (SAR) images to demonstrate the potential applicability
of our approach. 相似文献
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Bullmore E Fadili J Breakspear M Salvador R Suckling J Brammer M 《Statistical methods in medical research》2003,12(5):375-399
Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or 'whitening' of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by 'wavestrapping' of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT. 相似文献
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The undecimated wavelet decomposition and its reconstruction. 总被引:2,自引:0,他引:2
Jean-Luc Starck Jalal Fadili Fionn Murtagh 《IEEE transactions on image processing》2007,16(2):297-309
This paper describes the undecimated wavelet transform and its reconstruction. In the first part, we show the relation between two well known undecimated wavelet transforms, the standard undecimated wavelet transform and the isotropic undecimated wavelet transform. Then we present new filter banks specially designed for undecimated wavelet decompositions which have some useful properties such as being robust to ringing artifacts which appear generally in wavelet-based denoising methods. A range of examples illustrates the results. 相似文献
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Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities. 总被引:3,自引:0,他引:3
A novel Bayesian nonparametric estimator in the Wavelet domain is presented. In this approach, a prior model is imposed on the wavelet coefficients designed to capture the sparseness of the wavelet expansion. Seeking probability models for the marginal densities of the wavelet coefficients, the new family of Bessel K forms (BKF) densities are shown to fit very well to the observed histograms. Exploiting this prior, we designed a Bayesian nonlinear denoiser and we derived a closed form for its expression. We then compared it to other priors that have been introduced in the literature, such as the generalized Gaussian density (GGD) or the alpha-stable models, where no analytical form is available for the corresponding Bayesian denoisers. Specifically, the BKF model turns out to be a good compromise between these two extreme cases (hyperbolic tails for the alpha-stable and exponential tails for the GGD). Moreover, we demonstrate a high degree of match between observed and estimated prior densities using the BKF model. Finally, a comparative study is carried out to show the effectiveness of our denoiser which clearly outperforms the classical shrinkage or thresholding wavelet-based techniques. 相似文献
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Brain tissue classification of magnetic resonance images using partial volume modeling 总被引:1,自引:0,他引:1
This paper presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk alpha of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: 1) segmentation of the brain into pure and mixclasses using the mixture model; 2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences. 相似文献
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El Hanouti Imad El Fadili Hakim Zenkouar Khalid 《Multimedia Tools and Applications》2021,80(4):4975-4997
Multimedia Tools and Applications - Recently, a novel image encryption based on Arnold scrambling and Lucas series has been proposed in the literature. The scheme design is based on... 相似文献