论文标题
基于变压器的SAR图像preckling
Transformer-based SAR Image Despeckling
论文作者
论文摘要
合成孔径雷达（SAR）图像通常被称为Speckle的乘法噪声降解，该噪声使SAR图像的处理和解释变得困难。在本文中，我们介绍了一个基于变压器的网络，用于SAR Image Despeckling。拟议的phessckling网络包括一个基于变压器的编码器，该编码器允许网络学习不同图像区域之间的全局依赖关系 - 协助更好的伪装。该网络是通过合成生成的斑点图像的端到端训练的，使用复合损失函数。实验表明，所提出的方法对合成和真实SAR图像的传统和基于卷积神经网络的拼接方法取得了重大改进。
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.
