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]
< Reduce
Image, Modélisation, Analyse, GEométrie, Synthèse [IMAGES]
Département Images, Données, Signal [IDS]
Language
en
Communication dans un congrès
This item was published in
IGARSS, IGARSS, 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 2019-07-28, Yokohama. p. 5113-5116
IEEE
English Abstract
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 ...Read more >
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.Read less <
English Keywords
patches
non-local filtering
deep learning
speckle
SAR
PolSAR
Origin
Hal imported