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Synthetic aperture radar image despeckling with a residual learning of convolutional neural network

  • Published:2021-02 Issue: Volume:228 Page:165876
  • ISSN:0030-4026
  • Container-title:Optik
  • language:en
  • Short-container-title:Optik

Author:

Zhang MingORCID,Yang Li-dongORCID,Yu Da-huaORCID,An Ju-bai

Funder

National Natural Science Foundation of China

Research Program of Science and Technology at Universities of Inner Mongolia Autonomous Region

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference27 articles.

1. Benchmarking framework for SAR despeckling;Martino;IEEE Trans. Geosci. Remote Sens.,2014

2. Enhanced delaunay triangulation sea ice tracking algorithm with combining feature tracking and pattern matching;Zhang;Remote Sens.,2020

3. Skip-connected covariance network for remote sensing scene classification;He;IEEE Trans. Neural Netw. Learn. Syst.,2019

4. Scale-free convolutional neural network for remote sensing scene classification;Xie;IEEE Trans. Geosci. Remote Sens.,2019

5. SAR images denoising using a novel stochastic diffusion wavelet scheme;Ravi;Cluster Comput.,2017

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1. ANED-Net: Adaptive Noise Estimation and Despeckling Network for SAR Image;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

2. Edge-Constrained Guided Feature Perception Network for Ship Detection in SAR Images;IEEE Sensors Journal;2023-11-01

3. Physics-assisted Deep Learning for FMCW Radar Quantitative Imaging of Two-dimension Target;2023 8th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR);2023-10-23

4. CNN based color balancing and denoising technique for underwater images: CNN-CBDT;Measurement: Sensors;2023-08

5. A self-validation Noise2Noise training framework for image denoising;The Imaging Science Journal;2023-07-04
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