SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

Emanuele Dalsasso; Xiangli Yang; Loïc Denis; Florence Tupin; Wen Yang

doi:10.3390/rs12162636

Journal articles

SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

Emanuele Dalsasso ¹ Xiangli Yang Loïc Denis ² Florence Tupin ³ Wen Yang

1 IMAGES - Image, Modélisation, Analyse, GEométrie, Synthèse

LTCI - Laboratoire Traitement et Communication de l'Information

2 LHC - Laboratoire Hubert Curien [Saint Etienne]

3 LTCI - Laboratoire Traitement et Communication de l'Information

Document type :

Journal articles

Domain :

Computer Science [cs] / Neural and Evolutionary Computing [cs.NE]

Computer Science [cs] / Image Processing [eess.IV]

Complete list of metadata

https://hal.telecom-paris.fr/hal-02944565
Contributor : Emanuele Dalsasso Connect in order to contact the contributor
Submitted on : Monday, September 21, 2020 - 3:19:49 PM
Last modification on : Wednesday, November 3, 2021 - 6:22:48 AM

SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

Links full text

Identifiers

Collections

Citation

Export

Metrics

SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

Links full text

Identifiers

Collections

Citation

Export

Share

Metrics