论文标题
SAR Image Despeckling基于卷积Denoising AutoCoder
SAR Image Despeckling Based on Convolutional Denoising Autoencoder
论文作者
论文摘要
在合成孔径雷达（SAR）成像中，伪造对于图像分析非常重要，而斑点被称为由相干成像系统引起的一种乘法噪声。在过去的三十年中，已经提出了各种算法来确定SAR图像。通常，BM3D被认为是以出色的性能粉碎斑点噪声的最先进技术。最近，深度学习取得了成功，并在需要大型火车数据集的传统方法方面取得了改进。与大多数图像SAR图像伪装方法不同，提议的方法直接从损坏的图像中学习了斑点。在本文中，有限的数据集量表通过使用Convolutioal Denoising AutoCododer（C-DAE）重建无斑点的SAR图像，从而进行了有效的探索。批次归一化策略与C-DAE集成，以加快火车时间。此外，我们计算标准指标，PSNR和SSIM中的图像质量。据透露，我们的方法比其他方法表现良好。
In Synthetic Aperture Radar (SAR) imaging, despeckling is very important for image analysis,whereas speckle is known as a kind of multiplicative noise caused by the coherent imaging system. During the past three decades, various algorithms have been proposed to denoise the SAR image. Generally, the BM3D is considered as the state of art technique to despeckle the speckle noise with excellent performance. More recently, deep learning make a success in image denoising and achieved a improvement over conventional method where large train dataset is required. Unlike most of the images SAR image despeckling approach, the proposed approach learns the speckle from corrupted images directly. In this paper, the limited scale of dataset make a efficient exploration by using convolutioal denoising autoencoder (C-DAE) to reconstruct the speckle-free SAR images. Batch normalization strategy is integrated with C- DAE to speed up the train time. Moreover, we compute image quality in standard metrics, PSNR and SSIM. It is revealed that our approach perform well than some others.
