论文标题
一项模拟研究，以评估cauchy近端操作员的性能，以伪造海面的SAR图像
A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface
论文作者
论文摘要
海面的分析是使用合成孔径雷达（SAR）图像广泛进行的，因为它在挑战性的天气条件下，白天或夜晚在较广泛的区域中产生信息。斑点噪声构成了降低应用程序性能的主要原因，例如分类，船舶检测，目标跟踪等。本文提出了对包括船舶尾流结构的海洋图像的伪造的调查，该调查是通过使用Cauchy近端操作员的稀疏正规化来调查的。我们提出了一个封闭形式的表达式，用于计算Cauchy先验的近端算子，这使其适用于通用近端分裂算法。在我们的实验中，我们模拟了移动血管及其唤醒的SAR图像。与L1和TV规范正规化功能相比，评估了所提出的方法的性能。结果表明，针对所有使用的图像生成的图像的提出方法具有卓越的性能。
The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc. Speckle noise constitutes however the main reason for reduced performance in applications such as classification, ship detection, target tracking and so on. This paper presents an investigation into the despeckling of SAR images of the ocean that include ship wake structures, via sparse regularisation using the Cauchy proximal operator. We propose a closed-form expression for calculating the proximal operator for the Cauchy prior, which makes it applicable in generic proximal splitting algorithms. In our experiments, we simulate SAR images of moving vessels and their wakes. The performance of the proposed method is evaluated in comparison to the L1 and TV norm regularisation functions. The results show a superior performance of the proposed method for all the utilised images generated.
