Generative adversarial network-based restoration of speckled SAR images

Puyang Wang, He Zhang, Vishal M. Patel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Scopus citations

Abstract

Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Generative Adversarial Network (ID-GAN), for automatically removing speckle from the input noisy images. In particular, ID-GAN is trained in an end-to-end fashion using a combination of Euclidean loss, Perceptual loss and Adversarial loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Control and Optimization
  • Instrumentation

Keywords

  • Generative adversarial network
  • denoising
  • despecking
  • image restoration
  • perceptual loss
  • synthetic aperture radar

Fingerprint

Dive into the research topics of 'Generative adversarial network-based restoration of speckled SAR images'. Together they form a unique fingerprint.

Cite this