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José M. Bioucas-Dias

  1. Bayesian Segmentation of Oceanic SAR Images: Application to Oil Spill Detection.

    Thu, 07/29/2010 - 15:01 — SomNambul
    Authors: José M. Bioucas-Dias, Sónia Pelizzari
    Subjects: Applications
    Abstract

    This paper introduces Bayesian supervised and unsupervised segmentation
    algorithms aimed at oceanic segmentation of SAR images. The data term,
    \emph{i.e}., the density of the observed backscattered signal given the region,
    is modeled by a finite mixture of Gamma densities with a given predefined
    number of components. To estimate the parameters of the class conditional
    densities, a new expectation maximization algorithm was developed. The prior is
    a multi-level logistic Markov random field enforcing local continuity in a
    statistical sense.

  2. Alternating Direction Algorithms for Constrained Sparse Regression: Application to Hyperspectral Unmixing.

    Thu, 02/25/2010 - 16:01 — SomNambul
    Authors: José M. Bioucas-Dias, Mário A. T. Figueiredo
    Subjects: Optimization and Control
    Abstract

    Convex optimization problems are common in hyperspectral unmixing. Examples
    are the constrained least squares (CLS) problem used to compute the fractional
    abundances in a linear mixture of known spectra, the constrained basis pursuit
    (CBP) to find sparse (i.e., with a small number of terms) linear mixtures of
    spectra, selected from large libraries, and the constrained basis pursuit
    denoising (CBPDN), which is a generalization of BP to admit modeling errors.

  3. An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems.

    Fri, 12/18/2009 - 16:01 — SomNambul
    Authors: Manya V. Afonso, José M. Bioucas-Dias, Mário A. T. Figueiredo
    Subjects: Optimization and Control
    Abstract

    We propose a new fast algorithm for solving one of the standard approaches to
    ill-posed linear inverse problems (IPLIP), where a (possibly non-smooth)
    regularizer is minimized under the constraint that the solution explains the
    observations sufficiently well. Although the regularizer and constraint are
    usually convex, several particular features of these problems (huge
    dimensionality, non-smoothness) preclude the use of off-the-shelf optimization
    tools and have stimulated a considerable amount of research.

  4. Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization.

    Thu, 12/10/2009 - 16:01 — SomNambul
    Authors: José M. Bioucas-Dias, Mário A. T. Figueiredo
    Subjects: Optimization and Control
    Abstract

    Multiplicative noise (also known as speckle noise) models are central to the
    study of coherent imaging systems, such as synthetic aperture radar and sonar,
    and ultrasound and laser imaging. These models introduce two additional layers
    of difficulties with respect to the standard Gaussian additive noise scenario:
    (1) the noise is multiplied by (rather than added to) the original image; (2)
    the noise is not Gaussian, with Rayleigh and Gamma being commonly used
    densities.

  5. Fast Image Recovery Using Variable Splitting and Constrained Optimization.

    Tue, 10/27/2009 - 16:01 — SomNambul
    Authors: Manya V. Afonso, José M. Bioucas-Dias, Mário A. T. Figueiredo
    Subjects: Optimization and Control
    Abstract

    We propose a new fast algorithm for solving one of the standard formulations
    of image restoration and reconstruction which consists of an unconstrained
    optimization problem where the objective includes an $\ell_2$ data-fidelity
    term and a non-smooth regularizer. This formulation allows both wavelet-based
    (with orthogonal or frame-based representations) regularization or
    total-variation regularization. Our approach is based on a variable splitting
    to obtain an equivalent constrained optimization formulation, which is then
    addressed with an augmented Lagrangian method.

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