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Synthetic Aperture Radar (SAR) images contain a huge amount of information,however, the number of practical use-cases is limited due to the presence ofspeckle noise in them. In recent years, deep learning based techniques havebrought significant improvement in the domain of denoising and imagerestoration. However, further research has been hampered by the lack ofavailability of data suitable for training deep neural network based systems.With this paper, we propose a standard way of generating synthetic data for thetraining of speckle reduction algorithms and demonstrate a use-case to advanceresearch in this domain.

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