[Todos CMAT] Seminario de Probabilidad y Estadística -- Viernes 23 de octubre

[Andrés Sosa]

Hola

Este viernes 23 de octubre a las 10:00 horas en el salón de seminarios del
Centro de Matemática hablará* Pablo Musé *(Instituto de Ingeniería
Eléctrica -- Facultad de Ingeniería) en el seminario de Probabilidad y
Estadística.

El título de la charla es: *Estimación de imágenes de alto rango dinámico:
cotas de performance y algoritmos de generación. *

La charla constará de dos partes. Se abordará la primera parte  descrita
por el abstract  y si el tiempo lo permite la  segunda parte.


Saludos
Andrés

*Abstract:*

*Parte 1: Cotas de performance para el problema de estimación de imágenes
HDR*

*Since the seminal work of Mann and Picard in 1995, the standard way to
build high dynamic range (HDR) images from regular cameras has been to
combine a reduced number of photographs captured with different exposure
times. The algorithms proposed in the literature differ in the strategy
used to combine these frames. Several experimental studies comparing their
performances have been reported, showing in particular that a maximum
likelihood estimation yields the best results in terms of mean squared
error. However, no theoretical study aiming at establishing the performance
limits of the HDR estimation problem has been conducted. *

*Another common aspect of all HDR estimation approaches is that they
discard saturated values. In this paper, we address these two issues. More
precisely, we derive theoretical bounds for the performance of
unbiased estimators for the HDR estimation problem. The unbiasedness
hypothesis is motivated by the fact that most of the existing estimators,
among them the best performing and most well known, are nearly unbiased.
Moreover, we show that, even with a small number of photographs, the
maximum likelihood estimator performs extremely close to these bounds. *

*As a second contribution, we propose a general strategy for integrating
the information provided by saturated pixels in the estimation process,
hence improving the estimation results. Finally, we analyze the sensitivity
of the HDR estimation process to camera parameters, and we show that small
errors in the camera calibration process may severely degrade the
estimation results.*

*Parte 2: Un algoritmo de generación de imágenes HDR*

*High dynamic range (HDR) images are usually generated by combining
multiple photographs acquired with different exposure times. This approach,
while effective, suffers from various drawbacks. The irradiance estimation
is performed by combining, for each pixel, different exposure values at the
same spatial position. This estimation scheme does not take advantage of
the redundancy present in most images. Moreover, images must be perfectly
aligned and objects must be in the exact same position in all frames in
order to combine the different exposures. *

*In this work, we propose a new HDR image generation approach that
simultaneously copes with these problems and exploits image redundancy to
produce a denoised result. A reference image is chosen and a patch-based
approach is used to find similar pixels that are then combined for the
irradiance estimation. This patch-based approach permits to obtain
a denoised result and is robust to image misalignments and object motions. *

*Results show significant improvements in terms of noise reduction over
previous HDR image generation techniques, while being robust to motion and
changes between the exposures.*
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