Synthetic Aperture Radar (SAR) images are usually degraded by a multiplicative noise known as speckle which makes processing and interpretation of SAR images difficult. In this paper, we introduce a transformer-based network for SAR image despeckling. The proposed despeckling network comprises of a transformer-based encoder which allows the network to learn global dependencies between different image regions - aiding in better despeckling. The network is trained end-to-end with synthetically generated speckled images using a composite loss function. Experiments show that the proposed method achieves significant improvements over traditional and convolutional neural network-based despeckling methods on both synthetic and real SAR images.
@article{arxiv.2201.09355,
title = {Transformer-based SAR Image Despeckling},
author = {Malsha V. Perera and Wele Gedara Chaminda Bandara and Jeya Maria Jose Valanarasu and Vishal M. Patel},
journal= {arXiv preprint arXiv:2201.09355},
year = {2022}
}
Comments
Submitted to International Geoscience and Remote Sensing Symposium (IGARSS), 2022. Our code is available at https://github.com/malshaV/sar_transformer