Yangyang Wang , Liming Zhou , Xu Zhan , Guohao Sun , Yuxuan Liu
{"title":"3D mmW sparse imaging via complex-valued composite penalty function within collaborative multitasking framework","authors":"Yangyang Wang , Liming Zhou , Xu Zhan , Guohao Sun , Yuxuan Liu","doi":"10.1016/j.sigpro.2025.109939","DOIUrl":null,"url":null,"abstract":"<div><div>The emerging three-dimensional (3D) millimeter-wave (mmW) array SAR imaging with compressed sensing (CS) has shown impressive potential for improving image quality. However, the widely used <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> penalty function belongs to convex operators, which introduce bias effects in imaging and reduce reconstruction accuracy. In the context of 3D imaging, a single <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> is inadequate for characterizing the spatial features of the target, resulting in the loss of information. Additionally, the complex-valued nature of SAR data should be considered to further improve imaging performance. Therefore, in this article, a 3D sparse imaging method based on the complex-valued composite penalty function (CCPF) is proposed. Firstly, a CCPF is presented, which combines complex-valued minimax convex penalty (CMCP) and complex-valued 3D total variation (C3DTV) to alleviate bias effects while preserving the spatial structure information of the target. Secondly, the improved collaborative multitasking framework based on variable splitting and alternating minimization is presented to solve optimization problems with CCPF. Furthermore, the proposed method takes into account the complex-valued characteristics of SAR data and preserves the phase information of the imaging scene, which is beneficial for subsequent image interpretation. Finally, the effectiveness of the proposed method has been validated by a substantial amount of experimental data.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"233 ","pages":"Article 109939"},"PeriodicalIF":3.4000,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0165168425000544","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The emerging three-dimensional (3D) millimeter-wave (mmW) array SAR imaging with compressed sensing (CS) has shown impressive potential for improving image quality. However, the widely used penalty function belongs to convex operators, which introduce bias effects in imaging and reduce reconstruction accuracy. In the context of 3D imaging, a single is inadequate for characterizing the spatial features of the target, resulting in the loss of information. Additionally, the complex-valued nature of SAR data should be considered to further improve imaging performance. Therefore, in this article, a 3D sparse imaging method based on the complex-valued composite penalty function (CCPF) is proposed. Firstly, a CCPF is presented, which combines complex-valued minimax convex penalty (CMCP) and complex-valued 3D total variation (C3DTV) to alleviate bias effects while preserving the spatial structure information of the target. Secondly, the improved collaborative multitasking framework based on variable splitting and alternating minimization is presented to solve optimization problems with CCPF. Furthermore, the proposed method takes into account the complex-valued characteristics of SAR data and preserves the phase information of the imaging scene, which is beneficial for subsequent image interpretation. Finally, the effectiveness of the proposed method has been validated by a substantial amount of experimental data.
期刊介绍:
Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.
Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.