文献类型：期刊文献

英文题名：ACGNet: An Alternating Conjugate Gradient Optimization-Based Neural Network for SAR Image Despeckling

作者：Mao, Xin[1];Liu, Ying[1];Qiu, Chenghao[1,2,3];Lin, Cong[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Univ Elect Sci & Technol China, Sch Life Sci & Technol, Chengdu 610000, Peoples R China;[3]Guangdong Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2025

卷号：18

起止页码：13862

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:001504157200009)、、EI(收录号：20252218518366)、Scopus(收录号：2-s2.0-105006503457)、WOS

基金：This work was supported in part by the National Natural Science Foundationof China under Grant 62272109, in part by the Natural Science Foundation of Guangdong Province under Grant 2025A1515011356, in part by the Stable Supporting Fund of Acoustic Science and Technology Laboratory under Grant JCKYS2024604SSJS00301, in part by the Open Fund of Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching under Grant 2023B1212030003, in part by the program for scientific research start-up funds of Guangdong Ocean University under Grant 060302112405,in part by the Hainan Province Science and Technology Special Fund underGrant ATIC-202302001, and in part by the Undergraduate Innovation Team Project of Guangdong Ocean University under Grant CXTD2024011 and Grant JDTD2024003.

语种：英文

外文关键词：Noise; Speckle; Noise reduction; Optimization; Radar polarimetry; Image edge detection; Training; Synthetic aperture radar; Mathematical models; Computational modeling; Conjugate gradient (CG) optimization; despeckling method; edge correction function; synthetic aperture radar (SAR)

外文摘要：Synthetic aperture radar (SAR) images are characterized by unique speckle noise, and maintaining image details while effectively reducing this noise has always been a challenging problem. Current deep-learning-based denoising methods mainly rely on global noise models or local feature learning, but these methods often fail to achieve a balance between noise suppression and detail preservation. To address this issue, this article proposes a supervised collaborative denoising method with alternating optimization, which combines the alternating conjugate gradient method with an SAR despeckling network trained on paired noisy-clean simulated SAR data to progressively optimize image quality, thereby effectively reducing noise while preserving more details and texture information in the image. In addition, to enhance the feature learning capability of the denoising network, a dynamic edge-guided attention module is introduced. This module enhances feature learning capability and improves image detail extraction by guiding network decision making through adaptive weights. To better preserve the texture details of the image, an edge correction function is designed, incorporating edge correction terms and regularization terms into the traditional mean squared error loss function, thereby effectively improving the performance of the denoising results in edge regions. The proposed supervised framework is trained end-to-end on simulated SAR datasets with ground-truth references and demonstrates exceptional performance on both simulated datasets and real SAR images, as evidenced by extensive experimental results. Furthermore, comparative analysis highlights the substantial advantages of the method in both visual quality and quantitative metrics over classical and modern despeckling techniques.