From Patches to Deep Learning: Combining Self-Similarity and Neural Networks for Sar Image Despeckling
TUPIN, Florence
Image, Modélisation, Analyse, GEométrie, Synthèse [IMAGES]
Département Images, Données, Signal [IDS]
Image, Modélisation, Analyse, GEométrie, Synthèse [IMAGES]
Département Images, Données, Signal [IDS]
TUPIN, Florence
Image, Modélisation, Analyse, GEométrie, Synthèse [IMAGES]
Département Images, Données, Signal [IDS]
< Réduire
Image, Modélisation, Analyse, GEométrie, Synthèse [IMAGES]
Département Images, Données, Signal [IDS]
Langue
en
Communication dans un congrès
Ce document a été publié dans
IGARSS, IGARSS, 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 2019-07-28, Yokohama. p. 5113-5116
IEEE
Résumé en anglais
Speckle reduction has benefited from the recent progress in image processing, in particular patch-based non-local filtering and deep learning techniques. These two families of methods offer complementary characteristics ...Lire la suite >
Speckle reduction has benefited from the recent progress in image processing, in particular patch-based non-local filtering and deep learning techniques. These two families of methods offer complementary characteristics but have not yet been combined. We explore strategies to make the most of each approach.< Réduire
Mots clés en anglais
patches
non-local filtering
deep learning
speckle
SAR
PolSAR
Origine
Importé de halUnités de recherche