Search Results for author: Miguel Simões

Found 7 papers, 1 papers with code

Lasry-Lions Envelopes and Nonconvex Optimization: A Homotopy Approach

no code implementations15 Mar 2021 Miguel Simões, Andreas Themelis, Panagiotis Patrinos

Lasry-Lions envelopes can also be seen as an "intermediate" between a given function and its convex envelope, and we make use of this property to develop a method that builds a sequence of approximate subproblems that are easier to solve than the original problem.

GeoStat Representations of Time Series for Fast Classification

no code implementations13 Jul 2020 Robert J. Ravier, Mohammadreza Soltani, Miguel Simões, Denis Garagic, Vahid Tarokh

GeoStat representations are based off of a generalization of recent methods for trajectory classification, and summarize the information of a time series in terms of comprehensive statistics of (possibly windowed) distributions of easy to compute differential geometric quantities, requiring no dynamic time warping.

Classification Dynamic Time Warping +4

An Extension of Averaged-Operator-Based Algorithms

no code implementations12 Jun 2018 Miguel Simões, José Bioucas-Dias, Luis B. Almeida

Many of the algorithms used to solve minimization problems with sparsity-inducing regularizers are generic in the sense that they do not take into account the sparsity of the solution in any particular way.

A Framework for Fast Image Deconvolution with Incomplete Observations

1 code implementation3 Feb 2016 Miguel Simões, Luis B. Almeida, José Bioucas-Dias, Jocelyn Chanussot

In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast.

Demosaicking Image Deconvolution

Hyperspectral pansharpening: a review

no code implementations17 Apr 2015 Laetitia Loncan, Luis B. Almeida, José M. Bioucas-Dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio A. Licciardi, Miguel Simões, Jean-Yves Tourneret, Miguel A. Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya

In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data.


A convex formulation for hyperspectral image superresolution via subspace-based regularization

no code implementations14 Nov 2014 Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot

Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution.

Hyperspectral image superresolution: An edge-preserving convex formulation

no code implementations31 Mar 2014 Miguel Simões, José Bioucas-Dias, Luis B. Almeida, Jocelyn Chanussot

Hyperspectral remote sensing images (HSIs) are characterized by having a low spatial resolution and a high spectral resolution, whereas multispectral images (MSIs) are characterized by low spectral and high spatial resolutions.

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