Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms
This addresses the data scarcity issue for researchers in SAR image processing, though it is incremental as it builds on existing synthetic data generation methods.
The paper tackles the problem of limited data for training deep learning speckle noise reduction algorithms in SAR images by proposing a standard method for generating synthetic datasets, and demonstrates its use to advance research in this domain.
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.