15,587 research outputs found
Frames for Exact Inversion of the Rank Order Coder
International audienceOur goal is to revisit rank order coding by proposing an original exact decoding procedure for it. Rank order coding was proposed by Thorpe . who stated that the order in which the retina cells are activated encodes for the visual stimulus. Based on this idea, the authors proposed in a rank order coder/decoder associated to a retinal model. Though, it appeared that the decoding procedure employed yields reconstruction errors that limit the model bit-cost/quality performances when used as an image codec. The attempts made in the literature to overcome this issue are time consuming and alter the coding procedure, or are lacking mathematical support and feasibility for standard size images. Here we solve this problem in an original fashion by using the frames theory, where a frame of a vector space designates an extension for the notion of basis. Our contribution is twofold. First, we prove that the analyzing filter bank considered is a frame, and then we define the corresponding dual frame that is necessary for the exact image reconstruction. Second, to deal with the problem of memory overhead, we design a recursive out-of-core blockwise algorithm for the computation of this dual frame. Our work provides a mathematical formalism for the retinal model under study and defines a simple and exact reverse transform for it with over than 265 dB of increase in the peak signal-to-noise ratio quality compared to . Furthermore, the framework presented here can be extended to several models of the visual cortical areas using redundant representations
Approximate Decoding Approaches for Network Coded Correlated Data
This paper considers a framework where data from correlated sources are
transmitted with help of network coding in ad-hoc network topologies. The
correlated data are encoded independently at sensors and network coding is
employed in the intermediate nodes in order to improve the data delivery
performance. In such settings, we focus on the problem of reconstructing the
sources at decoder when perfect decoding is not possible due to losses or
bandwidth bottlenecks. We first show that the source data similarity can be
used at decoder to permit decoding based on a novel and simple approximate
decoding scheme. We analyze the influence of the network coding parameters and
in particular the size of finite coding fields on the decoding performance. We
further determine the optimal field size that maximizes the expected decoding
performance as a trade-off between information loss incurred by limiting the
resolution of the source data and the error probability in the reconstructed
data. Moreover, we show that the performance of the approximate decoding
improves when the accuracy of the source model increases even with simple
approximate decoding techniques. We provide illustrative examples about the
possible of our algorithms that can be deployed in sensor networks and
distributed imaging applications. In both cases, the experimental results
confirm the validity of our analysis and demonstrate the benefits of our low
complexity solution for delivery of correlated data sources
Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed kt-CSLDS, to
accelerate the image acquisition process of dynamic magnetic resonance imaging
(MRI). We are inspired by a state-of-the-art model for video compressive
sensing that utilizes a linear dynamical system (LDS) to model the motion
manifold. Given compressive measurements, the state sequence of an LDS can be
first estimated using system identification techniques. We then reconstruct the
observation matrix using a joint structured sparsity assumption. In particular,
we minimize an objective function with a mixture of wavelet sparsity and joint
sparsity within the observation matrix. We derive an efficient convex
optimization algorithm through alternating direction method of multipliers
(ADMM), and provide a theoretical guarantee for global convergence. We
demonstrate the performance of our approach for video compressive sensing, in
terms of reconstruction accuracy. We also investigate the impact of various
sampling strategies. We apply this framework to accelerate the acquisition
process of dynamic MRI and show it achieves the best reconstruction accuracy
with the least computational time compared with existing algorithms in the
literature.Comment: 30 pages, 9 figure
Adaptive low rank and sparse decomposition of video using compressive sensing
We address the problem of reconstructing and analyzing surveillance videos
using compressive sensing. We develop a new method that performs video
reconstruction by low rank and sparse decomposition adaptively. Background
subtraction becomes part of the reconstruction. In our method, a background
model is used in which the background is learned adaptively as the compressive
measurements are processed. The adaptive method has low latency, and is more
robust than previous methods. We will present experimental results to
demonstrate the advantages of the proposed method.Comment: Accepted ICIP 201
Frames for Exact Inversion of the Rank Order Coder
Our goal is to revisit rank order coding by proposing an original exact decoding procedure for it. Rank order coding was proposed by Simon Thorpe et al. who stated that the retina represents the visual stimulus by the order in which its cells are activated. A classical rank order coder/decoder was then designed on this basis [1]. Though, it appeared that the decoding proce- dure employed yields reconstruction errors that limit the model Rate/Quality performances when used as an image codec. The attempts made in the litera- ture to overcome this issue are time consuming and alter the coding procedure, or are lacking mathematical support and feasibility for standard size images. Here we solve this problem in an original fashion by using the frames theory, where a frame of a vector space designates an extension for the notion of basis. First, we prove that the analyzing filter bank considered is a frame, and then we define the corresponding dual frame that is necessary for the exact image reconstruction. Second, to deal with the problem of memory overhead, we de- sign a recursive out-of-core blockwise algorithm for the computation of this dual frame. Our work provides a mathematical formalism for the retinal model under study and defines a simple and exact reverse transform for it with up to 270 dB of PSNR gain compared to [1]. Furthermore, the framework presented here can be extended to several models of the visual cortical areas using redundant representations.Notre objectif est de revisiter le codage d'images statiques par rang en proposant une procédure originale de décodage exact. Le codage par rang a été proposé par Simon Thorpe et al. qui a affirmé que la rétine représente le stimulus visuel par l'ordre selon lequel ses cellules sont activées. Un codeur par ordre classique ainsi que le décodeur ont ensuite été conçus se basant sur ces résultats [1]. Cependant, il s'avère que la procédure de décodage employé engendre des erreurs de reconstruction qui limitent les performances Débit / Qualité du modèle lorsqu'il est utilisé comme un codec d'images. Les tentatives proposées dans la littérature pour surmonter ce problème prennent du temps et modifie la procédure de codage, ou manquent d'apport mathématique et de faisabilité pour des images de tailles standards. Ici nous résolvons ce problème de façon originale en utilisant la théorie des "frames", où une frame d'un espace vectoriel désigne une extension de la notion de base. Tout d'abord, nous montrons que le banc de filtres d'analyse considéré est une frame, puis nous définissons la frame duale correspondante qui est nécessaire pour la reconstruction exacte de l'image. Deuxièmement, pour faire face au problème du débordement de mémoire, nous concevons un algorithme récursif, out-of-core, et opérant par blocs pour le calcul de cette frame duale. Notre travail fournit un formalisme mathématique pour le modèle de la rétine à l'étude et définit une inversion simple et exacte de la transformée bio-inspirée définie dans [1] avec un maximum de 270 dB de gain de PSNR par rapport au modèle originel. Par ailleurs, le travail présenté ici peut être étendu à plusieurs autres modèles de zones corticales visuelles utilisant des représentations redondantes
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