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Back-projection (BP) is a costly computational step in tomography image reconstruction such as positron emission tomography (PET). To reduce the computation time, this paper presents a pipelined, prefetch, and parallelized architecture for PET BP (3PAPET). The key feature of this architecture is its original memory access strategy, masking the high latency(More)
Acoustic imaging is a standard technique for mapping acoustic source powers and positions from limited observations on microphone sensors, which often causes an ill-conditioned inverse problem. In this article, we firstly improve the forward model of acoustic power propagation by considering background noises at the sensor array, and the propagation(More)
The reduction of image reconstruction time is needed to spread the use of PET for research and routine clinical practice. In this purpose, this article presents a hardware/software architecture for the acceleration of 3D backprojection based upon an efficient 2D backprojection. This architecture has been designed in order to provide a high level of(More)
In order to improve quality of 3D X-ray tomography reconstruction for Non Destructive Testing (NDT), we investigate in this paper hierarchical Bayesian methods. In NDT, useful prior information on the volume like the limited number of materials or the presence of homogeneous area can be included in the iterative reconstruction algorithms. In hierarchical(More)
A great number of image reconstruction algorithms, based on analytical filtered backprojection, are implemented for X-ray Computed Tomography (CT) [1, 3]. The limits of these methods appear when the number of projections is small, and/or not equidistributed around the object. In this specific context, iterative algebraic methods are implemented. A great(More)
Gauss-Markov-Potts models for images and its use in many image restoration and super-resolution problems have shown their effective use for Non Destructive Testing (NDT) applications. In this paper, we propose a 3D Gauss-Markov-Potts model for 3D CT for NDT applications. Thanks to this model, we are able to perform a joint reconstruction and segmentation of(More)
Acoustic imaging is a powerful technique for acoustic source localization and power reconstruction from limited noisy measurements at microphone sensors. But it inevitably confronts a very ill-posed inverse problem which causes unexpected solution uncertainty. Recently, the Bayesian inference methods using sparse priors have been effectively investigated.(More)
In order to improve the quality of X-ray Computed Tomography (CT) reconstruction for Non Destructive Testing (NDT), we propose a hierarchical prior modeling with a Bayesian approach. In this paper we present a new hierarchical structure for the inverse problem of CT by using a multivariate Student-t prior which enforces sparsity and preserves edges. This(More)
sparsity inducing priors in the frequency domain Olivier Schwander, José Picheral, Nicolas Gac, Ali Mohammad-Djafari, Daniel Blacodon Laboratoire des Signaux et Systèmes, CNRS-Supélec-Univ. Paris-sud Département Signal et Systèmes Électroniques, Supelec Onera Motivation • Noise source characterization: very important for aeronautic and automotive industry •(More)