Title :

Quantitative control of the error bounds of a fast super-resolution technique for microscopy and astronomy

Author(s) :

Chainais, Pierre [Auteur]
LAGIS-SI
Centrale Lille
Sequential Learning [SEQUEL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Pfennig, Pierre [Auteur]
Sequential Learning [SEQUEL]
LAGIS-SI
Centrale Lille
Leray, Aymeric [Auteur]
Institut de Recherche Interdisciplinaire [Villeneuve d'Ascq] [IRI]

Conference title :

Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP)

City :

Florence

Country :

Italie

Start date of the conference :

2014-05-04

Publication date :

2014-05-04

English keyword(s) :

error estimate
Index Terms— microscopy
super-resolution
confidence level

HAL domain(s) :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]
Informatique [cs]/Algorithme et structure de données [cs.DS]

English abstract : [en]

While the registration step is often problematic for super-resolution, many microscopes and telescopes are now equipped with a piezoelectric mechanical system which permits to ac-curately control their motion (down to ...
Show more >While the registration step is often problematic for super-resolution, many microscopes and telescopes are now equipped with a piezoelectric mechanical system which permits to ac-curately control their motion (down to nanometers). There-fore one can use such devices to acquire multiple images of the same scene at various controlled positions. Then a fast super-resolution algorithm [1] can be used for efficient super-resolution. However the minimal use of r 2 images for a resolution enhancement factor r is generally not sufficient to obtain good results. We propose to take several images at po-sitions randomly distributed close to each reference position. We study the number of images necessary to control the error resulting from the super-resolution algorithm by [1] due to the uncertainty on positions. The main result is a lower bound on the number of images to respect a given error upper bound with probability higher than a desired confidence level.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL