Sparse representations and dictionary learning: from image fusion to motion estimation - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2021

Sparse representations and dictionary learning: from image fusion to motion estimation

Résumé

The first part of this paper presents some works conducted with Jose Bioucas Dias for fusing high spectral resolution images (such as hyperspectral images) and high spatial resolution images (such as panchromatic or multispectral images) in order to build images with improved spectral and spatial resolutions. These works are related to Bayesian fusion strategies exploiting prior information about the target image to be recovered constructed by dictionary learning. Interestingly, these Bayesian image fusion methods can be adapted with limited changes to motion estimation in pairs or sequences of images. The second part of this paper explains how the work of Jose Bioucas Dias has been a source of inspiration for developing new Bayesian motion estimation methods for ultrasound images.
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hal-03130465 , version 1 (30-06-2023)

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Jean-Yves Tourneret, Adrian Basarab, Nora Leïla Ouzir, Qi Wei. Sparse representations and dictionary learning: from image fusion to motion estimation. 41st International Geoscience and Remote Sensing Symposium (IGARSS 2021), IEEE Geoscience and Remote Sensing Society, Jul 2021, Brussels / Virtual, Belgium. pp.25-28, ⟨10.1109/IGARSS47720.2021.9554890⟩. ⟨hal-03130465⟩
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