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]
< Leer menos
Image, Modélisation, Analyse, GEométrie, Synthèse [IMAGES]
Département Images, Données, Signal [IDS]
Idioma
en
Communication dans un congrès
Este ítem está publicado en
IGARSS, IGARSS, 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 2019-07-28, Yokohama. p. 5113-5116
IEEE
Resumen en inglés
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 ...Leer más >
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.< Leer menos
Palabras clave en inglés
patches
non-local filtering
deep learning
speckle
SAR
PolSAR
Orígen
Importado de HalCentros de investigación