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- W4312772608 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. Our code is available at: https://github.com/malshaV/sar_transformer" @default.
- W4312772608 created "2023-01-05" @default.
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- W4312772608 date "2022-07-17" @default.
- W4312772608 modified "2023-10-10" @default.
- W4312772608 title "Transformer-Based SAR Image Despeckling" @default.
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- W4312772608 doi "https://doi.org/10.1109/igarss46834.2022.9884596" @default.
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