Transformer-based SAR Image Despeckling

• URL: http://arxiv.org/abs/2201.09355v1
• Date: Sun, 23 Jan 2022 20:09:01 GMT
• Title: Transformer-based SAR Image Despeckling
• Authors: Malsha V. Perera, Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, and Vishal M. Patel
• Abstract summary: 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. Experiments show that the proposed method achieves significant improvements over traditional and convolutional neural network-based despeckling methods.
• Score: 53.99620005035804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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