Learning a Dilated Residual Network for SAR Image Despeckling

作者：Zhang, Qiang; Yuan, Qiangqiang^*; Li, Jie; Yang, Zhen; Ma, Xiaoshuang

来源：Remote Sensing, 2018, 10(2): 196.

DOI：10.3390/rs10020196

• SAR image
• despeckling
• dilated convolution
• skip connection
• residual learning

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and a residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows a superior performance over the state-of-the-art methods in both quantitative and visual assessments, especially for strong speckle noise.

• 出版日期2018-2
• 单位武汉大学; 安徽大学