Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms
Image and Video Processing
2020-04-24 v1 Computer Vision and Pattern Recognition
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.
Keywords
Cite
@article{arxiv.2004.11021,
title = {Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms},
author = {Shrey Dabhi and Kartavya Soni and Utkarsh Patel and Priyanka Sharma and Manojkumar Parmar},
journal= {arXiv preprint arXiv:2004.11021},
year = {2020}
}
Comments
5 pages, 2 figures, 1 table