Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (14): 176-182.DOI: 10.3778/j.issn.1002-8331.1911-0060

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Improved Convolutional Neural Network for SAR Image Despeckling Algorithm

QIAN Man, ZHANG Xiangyang, LI Renchang   

  1. College of Information and Engineering, Nanchang HangKong University, Nanchang 330063, China
  • Online:2020-07-15 Published:2020-07-14



  1. 南昌航空大学 信息工程学院,南昌 330063


Synthetic Aperture Radar(SAR) is often suffered from a multiplicative noise commonly referred to as speckle which makes the interpretation of images difficult. To remove the speckle noise of SAR images, this paper proposes an improved convolutional neural networks approach for SAR image despeckling. The method firstly downsamples the image and then performs convolution to extract feature of the downsampled sub-image, which can effectively expand the receptive field and improve the denoising efficiency. In addition, skip connections and residual learning strategy are added to the despeckling model to reduce the vanishing gradient problem and improve the performance. Finally, simulated and real SAR images are utilized to test and evaluate the network. Experimental results show that compared with the start-of-art techniques, the proposed method achieves better performance and high efficiency.

Key words: Synthetic Aperture Radar(SAR) image despeckling, convolutional neural networks, image downsampling, skip connections, residual learning



关键词: 合成孔径雷达(SAR)图像去噪, 卷积神经网络, 图像下采样, 跳跃连接, 残差学习