文献类型：期刊文献

英文题名：A Deep Neural Network Based on Prior-Driven and Structural Preserving for SAR Image Despeckling

作者：Lin, Cong[1];Qiu, Chenghao[2];Jiang, Haoyu[1];Zou, Lilan[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Hainan Univ, Coll Informat & Commun Engn, Haikou 570228, Peoples R China

年份：2023

卷号：16

起止页码：6372

外文期刊名：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001036111000009)、、EI(收录号：20232814386172)、Scopus(收录号：2-s2.0-85164448733)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grant 62272109.

语种：英文

外文关键词：Deep image prior; speckle filtering; structural loss; synthetic aperture radar (SAR)

外文摘要：Remarkable effectiveness has been demonstrated by deep neural networks in the despeckling task for synthetic aperture radar (SAR) images. However, blurring and loss of fine details can result from many despeckling models due to upsampling and mean-square-error (MSE) loss. Additionally, existing degradation models and prior information are ignored by existing despeckling models, which directly learn the mapping from degraded to clear images. To address these issues, an optimization algorithm for the SAR despeckling task based on the integral-Newton method is proposed in this article. Then, a prior-driven despeckling network is proposed, which can automatically capture the implicit priors in SAR images to replace traditional manually made priors. Furthermore, to make the network focus more on learning the structural prior information of images, a structure-preserving loss function based on the MSE and the Canny edge detection operator is designed, which improves the detail of the network retention ability and speeds up convergence. Outstanding results on both simulated datasets and real SAR images are achieved by the proposed method, as shown by a large number of experimental results. Moreover, significant advantages of the proposed method both visually and quantitatively are revealed by comparison with classical and state-of-the-art despeckling algorithms.