论文标题
综合孔径雷达图像伪造的深度学习方法：趋势和观点的概述
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
论文作者
论文摘要
合成孔径雷达（SAR）图像受到称为Speckle的空间相关和信号依赖性噪声的影响，该噪声非常严重，可能会阻碍图像剥削。佩克林是一项重要的任务，旨在消除这种噪音，以提高所有下游图像处理任务的准确性。第一个伪装方法可以追溯到1970年代，随后几年已经开发了几种基于模型的算法。该领域受到了越来越多的关注，这是由于强大的深度学习模型的可用性而引起的，这些模型在图像处理中为反问题带来了出色的表现。本文调查了有关适用于SAR Despeckling的深度学习方法的文献，涵盖了被监督和最新的自我监督方法。我们提供对现有方法的批判性分析，目的是认识到最有前途的研究行，以确定限制深层模型成功的因素，并提出前进的方法，以尝试充分利用深度学习的潜力来sar desseckling。
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970's, and several model-based algorithms have been developed in the subsequent years. The field has received growing attention, sparkled by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This paper surveys the literature on deep learning methods applied to SAR despeckling, covering both the supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods with the objective to recognize the most promising research lines, to identify the factors that have limited the success of deep models, and to propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling.
