From Patches to Deep Learning: Combining Self-Similarity and Neural Networks for Sar Image Despeckling - Université Jean-Monnet-Saint-Étienne
Communication Dans Un Congrès Année : 2019

From Patches to Deep Learning: Combining Self-Similarity and Neural Networks for Sar Image Despeckling

Résumé

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.
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Dates et versions

ujm-03114605 , version 1 (19-01-2021)

Identifiants

Citer

Loïc Denis, Charles-Alban Deledalle, Florence Tupin. From Patches to Deep Learning: Combining Self-Similarity and Neural Networks for Sar Image Despeckling. 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), Jul 2019, Yokohama, Japan. pp.5113-5116, ⟨10.1109/IGARSS.2019.8898473⟩. ⟨ujm-03114605⟩
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