[Todos CMAT] Fwd: Defensa de tesis de maestría en ingeniería matemática de Mario González

Rafael Potrie rpotrie en cmat.edu.uy
Sab Dic 17 12:31:32 UYT 2016

---------- Forwarded message ----------
From: Pablo Musé <pmuse en fing.edu.uy>
Date: 2016-12-17 11:40 GMT-03:00
Subject: Defensa de tesis de maestría en ingeniería matemática de Mario
To: todos_iie en fing.edu.uy, "ingenieria. matematica" <
ingenieria.matematica en fing.edu.uy>, Rafael Potrie <rpotrie en cmat.edu.uy>,
Matias Di Martino <matiasdm en fing.edu.uy>, Rodrigo Alonso <
rodrigoa en fing.edu.uy>, Alvaro Pardo <apardo en ucu.edu.uy>
Cc: "\"Andrés Almansa (MAP5)\"" <andres.almansa en parisdescartes.fr>, Mario
González <mago876 en gmail.com>


Comparto con ustedes información sobre la defensa de la tesis de maestría
de Mario González, a quién tuve el placer de dirigir junto con Andrés
Almansa. Creo que puede ser de interés para varias personas de sus
institutos o departamentos, les pido que den difusión.



*Título**:* Processing wavelet compression artifacts in high-resolution
satellite imagery

*Hora y lugar**:* Jueves 22/12, 15h. Facultad de Ingeniería. Salón Rojo
(piso 7, salón 703).


JPEG and Wavelet compression artifacts leading to Gibbs effects and loss of
texture are well known and many restoration solutions exist in the
literature. So is denoising, which has occupied the image processing
community for decades. However, when a noisy image is compressed, a new
kind of artifact may appear from the interaction of both degradations. This
new kind of artifact is surprisingly never mentioned or studied in the
image processing community, with only a few rare exceptions. Yet the
importance of such artifacts in very high resolution satellite imaging has
recently been recognized. Indeed, such images are mainly used for highly
accurate sub-pixel stereo vision, an application where the presence of this
kind of artifacts (even if barely visible) is particularly harmful.

In this work we present a thorough probabilistic analysis of the kind of
degradation that results from the interaction of noise and compression
called wavelet outliers, and conclude that their probabilistic nature is
characterized by a single parameter *q/**σ* that can be inferred from a
noise model and a compression model. This analysis provides the conditional
probability for a Bayesian MAP estimator, whereas a patch-based local
Gaussian prior model is learnt from the corrupted image iteratively, like
in state of the art denoising algorithms (non-local Bayes), albeit with the
additional difficulty of dealing with non-Gaussian noise during the
learning process.

The resulting joint denoising and decompression algorithm has been
experimentally evaluated under realistic conditions. The results show its
ability to simultaneously denoise, decompress and remove wavelet outliers
better than the available alternatives, both from a quantitative and a
qualitative point of view. As expected, the advantage of our method is more
evident for large values of *q/**σ*, a situation that naturally occurs in
satellite images containing very dark areas (shadows).


Antoni Buades (*revisor externo*, Universitat de les Illes Balears, España)
Omar Gil (Universidad de la República, Uruguay)
Roberto Markarián (Universidad de la República, Uruguay)
Lionel Moisan (*revisor externo*,Université Paris Descartes, Francia)
Alvaro Pardo (Universidad Católica, Uruguay)
Ignacio Ramírez (Universidad de la República, Uruguay)
Andrés Almansa (*DT*, CNRS, U. Paris Descartes, Francia)
Pablo Musé (*DT*, Universidad de la República, Uruguay)

Pablo Musé, PhD
Full Professor of Signal Processing
Universidad de la República
+598 27110974 <+598%202711%200974>

Rafael Potrie
rafaelpotrie en gmail.com
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