Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Amandine Blanc

dblp:62/4269 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 50% Computational photography and imaging · 50%
Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing
image restoration
0.112005
Statistical behavior of joint least-square estimation in the phase diversity context · IEEE Trans. Image Process. 2005
Computational photography and imaging
wavefront sensing
0.112005
Statistical behavior of joint least-square estimation in the phase diversity context · IEEE Trans. Image Process. 2005
Mathematical optimization
least squares
0.112005
Statistical behavior of joint least-square estimation in the phase diversity context · IEEE Trans. Image Process. 2005

Methods — techniques the papers use, named apart from their topics

maximum likelihood estimation · 0.1asymptotic theory of toeplitz matrices · 0.1
YearPublicationVenuePosition
2005 Statistical behavior of joint least-square estimation in the phase diversity context
abstract
The images recorded by optical telescopes are often degraded by aberrations that induce phase variations in the pupil plane. Several wavefront sensing techniques have been proposed to estimate aberrated phases. One of them is phase diversity, for which the joint least-square approach introduced by Gonsalves et al. is a reference method to estimate phase coefficients from the recorded images. In this paper, we rely on the asymptotic theory of Toeplitz matrices to show that Gonsalves' technique provides a consistent phase estimator as the size of the images grows. No comparable result is yielded by the classical joint maximum likelihood interpretation (e.g., as found in the work by Paxman et al.). Finally, our theoretical analysis is illustrated through simulated problems.
Jérôme Idier, Laurent M. Mugnier, Amandine Blanc
IEEE Trans. Image Process.3