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- W4312835575 abstract "Acquired speckle noise degrades the quality of synthetic aperture radar (SAR) images which affect their further processing and analysis. In this paper we propose a sharp image feature based SAR denoising network called SIFSDNet. This network uses a image sharpening block, which amplifies the feature content of the input noisy image. Features of this sharpened image is then extracted and concatenated along with the features of the input noisy image to get a combined feature map. The despeckled SAR image is then reconstructed from the combined feature map. The proposed method supersedes state-of-the-art classical as well as deep learning based SAR denoising methods, both in terms of visual results as well as quantitative analysis. We have shared the code of SIFSDNet at https://github.com/RTSIR/SIFSDNet." @default.
- W4312835575 created "2023-01-05" @default.
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- W4312835575 date "2022-07-17" @default.
- W4312835575 modified "2023-10-16" @default.
- W4312835575 title "SIFSDNet: Sharp Image Feature Based SAR Denoising Network" @default.
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- W4312835575 doi "https://doi.org/10.1109/igarss46834.2022.9883415" @default.
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