Transformer-based SAR Image Despeckling

Malsha V. Perera; Wele Gedara Chaminda Bandara; Jeya Maria Jose Valanarasu; Vishal M. Patel

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arXiv · 2201.09355

Transformer-based SAR Image Despeckling

Malsha V. Perera·Wele Gedara Chaminda Bandara·Jeya Maria Jose Valanarasu·Vishal M. Patel

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Abstract

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.

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BibTeX RIS

Malsha V. Perera, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, Vishal M. Patel. 2022-01-23. Transformer-based SAR Image Despeckling. https://arxiv.org/abs/2201.09355

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