Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms

• URL: http://arxiv.org/abs/2004.11021v1
• Date: Thu, 23 Apr 2020 08:27:45 GMT
• Title: Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms
• Authors: Shrey Dabhi, Kartavya Soni, Utkarsh Patel, Priyanka Sharma and Manojkumar Parmar
• Abstract summary: We propose a standard way of generating synthetic data for the training of speckle reduction algorithms. We demonstrate a use-case to advance research in this domain.
• Score: 3.0448872422956432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic Aperture Radar (SAR) images contain a huge amount of information, however, the number of practical use-cases is limited due to the presence of speckle noise in them. In recent years, deep learning based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network based systems. With this paper, we propose a standard way of generating synthetic data for the training of speckle reduction algorithms and demonstrate a use-case to advance research in this domain.

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