Joint Learning of Fully Connected Network Models in Lifting Based Image Coders.

The optimization of prediction and update operators plays a prominent role in lifting-based image coding schemes. In this paper, we focus on learning the prediction and update models involved in a recent Fully Connected Neural Network (FCNN)-based lifting structure. While a straightforward approach consists in separately learning the different FCNN models by optimizing appropriate loss functions, jointly learning those models is a more challenging problem.

Dynamic Neural Network for Lossy-to-Lossless Image Coding.

Lifting-based wavelet transform has been extensively used for efficient compression of various types of visual data. Generally, the performance of such coding schemes strongly depends on the lifting operators used, namely the prediction and update filters. Unlike conventional schemes based on linear filters, we propose, in this paper, to learn these operators by exploiting neural networks.

Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers.

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using an efficient classifier combination of feature engineering-based and feature learning-based data representation.

An Auxiliary Variable Method for Markov Chain Monte Carlo Algorithms in High Dimension.

In this paper, we are interested in Bayesian inverse problems where either the data fidelity term or the prior distribution is Gaussian or driven from a hierarchical Gaussian model. Generally, Markov chain Monte Carlo (MCMC) algorithms allow us to generate sets of samples that are employed to infer some relevant parameters of the underlying distributions. However, when the parameter space is high-dimensional, the performance of stochastic sampling algorithms is very sensitive to existing dependencies between parameters.

A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging.

To reduce scanning time and/or improve spatial/temporal resolution in some Magnetic Resonance Imaging (MRI) applications, parallel MRI acquisition techniques with multiple coils acquisition have emerged since the early 1990s as powerful imaging methods that allow a faster acquisition process. In these techniques, the full FOV image has to be reconstructed from the resulting acquired undersampled k-space data. To this end, several reconstruction techniques have been proposed such as the widely-used SENSitivity Encoding (SENSE) method.

Satellite image restoration in the context of a spatially varying point spread function.

In this paper, we consider a deconvolution problem where the point spread function (PSF) of the optical imaging system varies between different spatial locations, thus leading to a spatially varying blur. This problem arises, for example, in synthetic aperture instruments and in wide-field optical systems. Unlike the classical deconvolution context where the PSF is assumed to be spatially invariant, the problem cannot be easily solved in the Fourier domain.

Vector lifting schemes for stereo image coding.

Many 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 lossy-to-lossless coding schemes for stereo images allowing progressive reconstruction. The most commonly used approaches for stereo compression are based on disparity compensation techniques.

Quantification of microarchitecture bone junctions based on a dual tree M-band wavelet decomposition.

Osteoporosis shows itself both in a reduction of the bone mass and a degradation of the microarchitecture of the bone tissue. To this respect, radiographies of the calcaneus are used to analyze both the texture and the structure of the bone thanks to sophisticated image processing tools. In this paper, we propose a method for evaluating the number of junctions in the imaged microarchitecture.

Building robust wavelet estimators for multicomponent images using Stein's principle.

Multichannel imaging systems provide several observations of the same scene which are often corrupted by noise. In this paper, we are interested in multispectral image denoising in the wavelet domain. We adopt a multivariate statistical approach in order to exploit the correlations existing between the different spectral components.