论文标题
维护CNN SAR伪装算法的边缘的复杂性分析
Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm
论文作者
论文摘要
SAR图像受到造成解释的乘法噪声的影响。在过去的几十年中，已经提出了几种用于SAR DeNoising的方法，在过去的几年中，非常关注已朝着基于深度学习的解决方案迈进。基于我们最后提出的卷积神经网络，用于SAR Despeckling，在这里我们利用了网络复杂性的影响。更确切地说，一旦确定了数据集，我们就对网络数量和网络的功能数量进行了分析。对模拟和实际数据进行评估。结果表明，更深层次的网络可以更好地概括模拟图像和真实图像。
SAR images are affected by multiplicative noise that impairs their interpretations. In the last decades several methods for SAR denoising have been proposed and in the last years great attention has moved towards deep learning based solutions. Based on our last proposed convolutional neural network for SAR despeckling, here we exploit the effect of the complexity of the network. More precisely, once a dataset has been fixed, we carry out an analysis of the network performance with respect to the number of layers and numbers of features the network is composed of. Evaluation on simulated and real data are carried out. The results show that deeper networks better generalize on both simulated and real images.
