L'axe analyse et traitement d'images développe des techniques robustes de traitement du signal et de l'image. Son activité sur ces dernières années a concerné la vision omnidirectionnelle, la reconstruction d'images de tomographie d'émission et d'images super-résolues, l'estimation statistique et l'imagerie hyperspectrale et satellitaire.
In video surveillance, microscopy, image restoration and many other
applications, High-Resolution (HR) images are required when only
Low-Resolution (LR) images are available. This situation arises when
details to be analyzed are beyond the resolution of the imager.
Multi-Frame Super-Resolution (MFSR) is a way to overcome this problem.
La super-résolution est une technique de traitement d’images qui consiste en la reconstruction d’une image hautement résolue à partir d’une ou plusieurs images bassement résolues. Cette technique est apparue dans les années 1980 pour tenter d’augmenter artificiellement la résolution des images et donc de pallier, de façon algorithmique, les limites physiques des capteurs d’images. Comme beaucoup des techniques de reconstruction en traitement d’images, la super-résolution est connue pour être un problème mal posé dont la résolution numérique est mal conditionnée.
Since 2013, the Corsaire Concept Project has focused on the development of new tools and methods for deep underwater archaeology (from 50m to 2,000m). This project is led by the DRASSM (Département des Recherches Archéo-logiques Subaquatiques et Sous-Marines, French Ministry of Culture’s Department for Underwater Archaeology). LIRMM coordinates the robotic activities of this project and collaborates with several laboratories (Stanford Robotics, Institut PPrime, Onera DTIM, ENSTA Bretagne) and SMEs (Techno Concept, Becom-d, SIT, Copetech SM, Images Exploration). Several new robotic tools and methods have been introduced and tested under the supervision of the archaeologists.
A first theoretical contribution consisted in extending the classical Kolmogorov- Smirnov homogeneity test to compare two samples of interval-valued observed measurements. In such a case, the test result is interval-valued, and one major diffcultyis to find the bounds of this set. We propose a very efficient computational method for approximating these bounds by using a p-box (pairs of upper and lower cumulative distributions) representation of the samples.
Here we propose an adaption of Wilcoxon's two-sample rank-sum test to interval data.
These days, an increasing number of documents are distributed in digital format due to their easy transportation, archiving and hard copy reproduction. This fact increases the number of Valuable Document Counterfeits (VDC) as electronic versions of bills, bank checks and transport tickets.