Exploiting a priori known structural information lies at the core of many image reconstruction
methods that can be stated as inverse problems. The synthesis model, which assumes that …

Snapshot compressive imaging (SCI) is a promising approach to capture high-dimensional
data with low dimensional sensors. With modest modifications to off-the-shelf cameras, SCI …

This paper presents an algorithm that allows to learn low dimensional representations of
images in an unsupervised manner. The core idea is to combine two criteria that play …

There is an enhancement of network and communication and other technologies, which
leads to visualize various applications for common users. Medical systems are one of the …

The sparse synthesis model for signals has become very popular in the last decade, leading
to improved performance in many signal processing applications. This model assumes that …

This work studies the problem of blind sensor calibration (BSC) in linear inverse problems,
such as compressive sensing. It aims to estimate the unknown complex gains at each …

This thesis investigates the problem of learning sparse data models and their applications to
image processing, regarding both the synthesis and the analysis point of view. Two …

Identifying and evaluating functionally connected regions in the brain has become a
challenging problem to solve in many studies of neurological and psychiatric disorders. In …

The reconstruction of a signal from only a few measurements, deconvolving, or denoising
are only a few interesting signal processing applications that can be formulated as linear …