Despeckling is a crucial noise reduction task in improving the quality of
synthetic aperture radar (SAR) images. Directly obtaining noise-free SAR images
is a challenging task that has hindered the development of accurate despeckling
algorithms. The advent of deep learning has facilitated the study of denoising
models that learn from only noisy SAR images. However, existing methods deal
solely with single-polarization images and cannot handle the multi-polarization
images captured by modern satellites. In this work, we present an extension of
the existing model for generating single-polarization SAR images to handle
multi-polarization SAR images. Specifically, we propose a novel self-supervised
despeckling approach called channel masking, which exploits the relationship
between polarizations. Additionally, we utilize a spatial masking method that
addresses pixel-to-pixel correlations to further enhance the performance of our
approach. By effectively incorporating multiple polarization information, our
method surpasses current state-of-the-art methods in quantitative evaluation in
both synthetic and real-world scenarios.

利用多极化合成孔径雷达（SAR）图像，我们提出了一种名为通道屏蔽的自监督去斑方法，利用极化之间的关系和像素到像素的相关性处理，实现了比现有方法更好的去噪效果。

PolMERLIN：基于掩码网络的自监督极化复合 SAR 图像去斑

PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling  with Masked Networks

We propose a novel approach for generating high quality visible-like images
from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative
Adversarial Network (GAN) architectures. The proposed approach is based on a
cascaded network of convolutional neural nets (CNNs) for despeckling and image
colorization. The cascaded structure results in faster convergence during
training and produces high quality visible images from the corresponding SAR
images. Experimental results on both simulated and real SAR images show that
the proposed method can produce visible-like images better compared to the
recent state-of-the-art deep learning-based methods.

本文提出了一种基于生成对抗网络和级联卷积神经网络的新方法，用于从合成孔径雷达图像生成高质量的可见光图像，实验表明该方法在去斑和图像上色方面比目前的深度学习方法表现更好。