Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application

We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2. Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. Here we recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their benefit through a linear analysis. Using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matrices to diagnose the nature of the ill-posedness and evaluate regularisation strategies. We then study the non linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach

Constraining DALEC v2 using multiple data streams and ecological constraints: analysis and application.

We use a variational method to assimilate multiple data streams into the terrestrial ecosystem carbon cycle model DALECv2. Ecological and dynamical constraints have recently been introduced to constrain unresolved components of this otherwise ill-posed problem. Here we recast these constraints as a multivariate Gaussian distribution to incorporate them into the variational framework and we demonstrate their benefit through a linear analysis. Using an adjoint method we study a linear approximation of the inverse problem: firstly we perform a sensitivity analysis of the different outputs under consideration, and secondly we use the concept of resolution matrices to diagnose the nature of the ill-posedness and evaluate regularisation strategies.We then study the non linear problem with an application to real data. Finally, we propose a modification to the model: introducing a spin-up period provides us with a built-in formulation of some ecological constraints which facilitates the variational approach

Tsallis entropy measure of noise-aided information transmission in a binary channel

Noise-aided information transmission via stochastic resonance is shown and analyzed in a binary channel by means of information measures based on the Tsallis entropy. The analysis extends the classic reference of binary information transmission based on the Shannon entropy, and also parallels a recent study based on the Rényi entropy. The conditions for a maximally pronounced stochastic resonance identify optimal Tsallis measures. The study involves a correspondence between Tsallis and Rényi information measures, specially relevant to the characterization of stochastic resonance, and establishing that for such effects identical properties are shared in common by both Tsallis and Rényi measures

Exploiting the speckle noise for compressive imaging

An optical setup is proposed for the implementation of compressive sensing with coherent images. This setup specifically exploits the natural multiplicative action of speckle noise occurring with coherent light, in order to optically realize the essential step in compressive sensing which is the multiplication with known random patterns of the image to be acquired. In the test of the implementation, we specifically examine the impact of several departures, that exist in practice, from the ideal conditions of a pure multiplicative action of the speckle. In such practical realistic conditions, we assess the feasibility, performance and robustness of the optical scheme of compressive sensing. (C) 2011 Elsevier B.V. All rights reserved

Joint acquisition-processing approach to optimize observation scales in noisy imaging

In imaging, the choice of an observation scale is conventionally settled by the operator in charge of the image acquisition, who is left alone with tuning the framing and zooming parameters of the imaging system. In a somewhat decoupled manner, the operator in charge of processing the data has access to the images after their acquisition, and seeks to extract information from the observed scene. This Letter proposes a manifestation of the interest of an alternative joint acquisition-processing approach. We demonstrate with quantitative informational measures how the choice of an observation scale can be directly related to the performance of the final information processing task. Illustrations are given with various tools from statistical information theory with possible applications of practical interest to any noisy imaging domains

Structural Similarity Measure to Assess Improvement by Noise in Nonlinear Image Transmission

We show that the structural similarity index is able to register stochastic resonance or improvement by noise in nonlinear image transmission, and sometimes when not registered by traditional measures of image similarity, and that in this task this index remains in good match with the visual appreciation of image quality

Source coding with Tsallis entropy

An extension is presented to the source coding theorem traditionally based on the Shannon entropy and later generalised to the Rényi entropy. Another possible generalisation is demonstrated, with a lower bound realised by the Tsallis entropy, when the performance is measured by the generalised average coding length which is exhibited, and with the optimal codelengths expressed from the escort probability distribution, also known in nonextensive thermodynamics

Rényi entropy measure of noise-aided information transmission in a binary channel

This paper analyzes a binary channel by means of information measures based on the Rényi entropy. The analysis extends, and contains as a special case, the classic reference model of binary information transmission based on the Shannon entropy measure. The extended model is used to investigate further possibilities and properties of stochastic resonance or noise-aided information transmission. The results demonstrate that stochastic resonance occurs in the information channel and is registered by the Rényi entropy measures at any finite order, including the Shannon order. Furthermore, in definite conditions, when seeking the Rényi information measures that best exploit stochastic resonance, then nontrivial orders differing from the Shannon case usually emerge. In this way, through binary information transmission, stochastic resonance identifies optimal Rényi measures of information differing from the classic Shannon measure. A confrontation of the quantitative information measures with visual perception is also proposed in an experiment of noise-aided binary image transmission

Un critère informationnel en imagerie pour l’échelle optimale d’observation d’une scène bruitée

En imagerie, la question de l\u27échelle d\u27observation d\u27une scène estconventionnellement laissée à l\u27appréciation de l\u27expérimentateur qui a seul la charge du choix du grossissement du système imageur. De façon souvent découplée, le traiteur de données récupère les images après acquisition et, à partir de là, cherche à extraire aux mieux les informations dans la scène. Dans ce travail, nous illustrons sur un exemple l\u27intérêt d\u27une approche acquisition-traitement conjoint. Nous montrons au moyen d\u27outils quantitatifs issus de la théorie statistique de l\u27information comment le choix de l\u27échelle d\u27observation en imagerie peut être directement relié aux performances de la tâche finale de traitement de l\u27information. Le propos est illustré sur des systèmes d\u27imagerie bruitée utiles pour le domaine biomédical et l\u27instrumentation en optique cohérente

Constructive role of sensors nonlinearities in the acquisition of partially polarized speckle images

We study the impact of the level of the speckle noise on data acquisition in a partially polarized coherent imaging system with the presence of a nonlinearity in the imaging sensor characteristic. In perfectly linear acquisition conditions, due to the essentially multiplicative action of the speckle, the image contrast is unchanged as the speckle noise level increases, and so it has no impact on the quality of the acquired images. On the contrary, in nonlinear conditions the acquisition is affected by the speckle noise level. However, this effect of the speckle is not always detrimental. We show that, in definite nonlinear conditions, there is usually an optimal level of the speckle noise that leads to a maximum quality of the acquired images. We theoretically analyze such nonlinear regimes with partially polarized speckled images. We specifically exhibit the existence of an optimal speckle noise level in the interesting case of images realized only by a depolarization contrast. Illustrations are given with a simple 1-bit hard limiter and binary images. Then, we propose and discuss as perspectives an experimental optical setup to confront theory and experiment