75 research outputs found
A SURE Approach for Digital Signal/Image Deconvolution Problems
In this paper, we are interested in the classical problem of restoring data
degraded by a convolution and the addition of a white Gaussian noise. The
originality of the proposed approach is two-fold. Firstly, we formulate the
restoration problem as a nonlinear estimation problem leading to the
minimization of a criterion derived from Stein's unbiased quadratic risk
estimate. Secondly, the deconvolution procedure is performed using any analysis
and synthesis frames that can be overcomplete or not. New theoretical results
concerning the calculation of the variance of the Stein's risk estimate are
also provided in this work. Simulations carried out on natural images show the
good performance of our method w.r.t. conventional wavelet-based restoration
methods
Majorize-Minimize adapted Metropolis-Hastings algorithm. Application to multichannel image recovery
International audienceOne challenging task in MCMC methods is the choice of the proposal density. It should ideally provide an accurate approximation of the target density with a low computational cost. In this paper, we are interested in Langevin diffusion where the proposal accounts for a directional component. We propose a novel method for tuning the related drift term. This term is preconditioned by an adaptive matrix based on a Majorize-Minimize strategy. This new procedure is shown to exhibit a good performance in a multispectral image restoration example
Minimization of a sparsity promoting criterion for the recovery of complex-valued signals
International audienceIll-conditioned inverse problems are often encountered in signal/image processing. In this respect, convex objective functions including a sparsity promoting penalty term can be used. However, most of the existing optimization algorithms were developed for real-valued signals. In this paper, we are interested in complex-valued data. More precisely, we consider a class of penalty functions for which the associated regularized minimization problem can be solved numerically by a forward-backward algorithm. Functions within this class can be used to promote the sparsity of the solution. An application to parallel Magnetic Resonance Imaging (pMRI) reconstruction where complex-valued images are reconstructed is considered
A nonlinear Stein based estimator for multichannel image denoising
The use of multicomponent images has become widespread with the improvement
of multisensor systems having increased spatial and spectral resolutions.
However, the observed images are often corrupted by an additive Gaussian noise.
In this paper, we are interested in multichannel image denoising based on a
multiscale representation of the images. A multivariate statistical approach is
adopted to take into account both the spatial and the inter-component
correlations existing between the different wavelet subbands. More precisely,
we propose a new parametric nonlinear estimator which generalizes many reported
denoising methods. The derivation of the optimal parameters is achieved by
applying Stein's principle in the multivariate case. Experiments performed on
multispectral remote sensing images clearly indicate that our method
outperforms conventional wavelet denoising technique
Two-dimensional non separable adaptive lifting scheme for still and stereo image coding
International audienceMany existing works related to lossy-to-lossless image compression are based on the lifting concept. However, it has been observed that the separable lifting scheme structure presents some limitations because of the separable processing performed along the image lines and columns. In this paper, we propose to use a 2D non separable lifting scheme decomposition that enables progressive reconstruction and exact decoding of images. More precisely, we focus on the optimization of all the involved decomposition operators. In this respect, we design the prediction filters by minimizing the variance of the detail signals. Concerning the update filters, we propose a new optimization criterion which aims at reducing the inherent aliasing artefacts. Simulations carried out on still and stereo images show the benefits which can be drawn from the proposed optimization of the lifting operators
Vector Lifting Schemes for Stereo Image Coding
International audienceMany research efforts have been devoted to the improvement of stereo image coding techniques for storage or transmission. In this paper, we are mainly interested in lossyto- lossless coding schemes for stereo images allowing progressive reconstruction. The most commonly used approaches for stereo compression are based on disparity compensation techniques. The basic principle involved in this technique first consists of estimating the disparity map. Then, one image is considered as a reference and the other is predicted in order to generate a residual image. In this work, we propose a novel approach, based on Vector Lifting Schemes (VLS), which offers the advantage of generating two compact multiresolution representations of the left and the right views. We present two versions of this new scheme. A theoretical analysis of the performance of the considered VLS is also conducted. Experimental results indicate a significant improvement using the proposed structures compared with conventional methods
Adaptive lifting schemes with a global L1 minimization technique for image coding
International audienceMany existing works related to lossy-to-lossless image compression are based on the lifting concept. In this paper, we present a sparse op- timization technique based on recent convex algorithms and applied to the prediction filters of a two-dimensional non separable lifting structure. The idea consists of designing these filters, at each resolution level, by minimizing the sum of the â1-norm of the three detail subbands. Extending this optimization method in order to perform a global minimization over all resolution levels leads to a new opti- mization criterion taking into account linear dependencies between the generated coefficients. Simulations carried out on still images show the benefits which can be drawn from the proposed optimization techniques
Lifting schemes for joint coding of stereoscopic pairs of satellite images
electronic version (5 pp.)International audienceStereo data compression is an important issue for the new generation of vision systems. In this paper, we are interested in lossless coding methods for stereo images allowing progressive reconstruction. Most of the existing approaches account for the mutual similarities between the left and the right images. More precisely, the disparity compensation process consists in predicting the right image from the left one based on the disparity map. Then, the disparity map, the reference image, and the residual image are encoded. In this work, we propose a novel approach based on the concept of vector lifting scheme. Its main feature is that it does not generate one residual image but two compact multiresolution representations of the left and the right views, driven by the underlying disparity map. Experimental results show a signiïŹcant improvement using this technique compared with conventional methods
Bancs de filtres à 3 canaux et analyse échelle-espace non linéaire
Dans cet article, nous traitons du problÚme de la conception des opérateurs intervenant dans un banc de filtres non linéaires. L'originalité du travail réside dans le fait que nous utilisons comme moyen de conception des filtres non linéaires des analyses multi-échelles construites à partir d'équations aux dérivées partielles. Notre démarche nous amÚne à la mise en place d'une nouvelle structure de banc de filtres non linéaires à 3 canaux garantissant une reconstruction parfaite. Par ailleurs, nous nous intéressons à l'analyse, au moyen de la décomposition considérée, d'un signal particulier, qui présente une discontinuité, . Enfin, des résultats de simulation montrent le caractÚre prometteur de cette structure d'analyse dans le contexte du débruitage de signaux
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