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

Loïc Denis; Charles-Alban Deledalle; Florence Tupin

doi:10.1109/IGARSS.2019.8898473

Conference papers

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

Loïc Denis ¹ Charles-Alban Deledalle ² Florence Tupin ³

1 LHC - Laboratoire Hubert Curien [Saint Etienne]

2 IMB - Institut de Mathématiques de Bordeaux

3 LTCI - Laboratoire Traitement et Communication de l'Information

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 but have not yet been combined. We explore strategies to make the most of each approach.

Keywords : patches non-local filtering deep learning speckle SAR PolSAR

Document type :

Conference papers

Domain :

Engineering Sciences [physics] / Signal and Image processing

Computer Science [cs] / Machine Learning [cs.LG]

Complete list of metadata

https://hal-ujm.archives-ouvertes.fr/ujm-03114605
Contributor : Loïc Denis Connect in order to contact the contributor
Submitted on : Tuesday, January 19, 2021 - 9:51:15 AM
Last modification on : Tuesday, October 19, 2021 - 11:05:54 PM
Long-term archiving on: : Tuesday, April 20, 2021 - 6:21:53 PM

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

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

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166