Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization
In recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularizatio...
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MDPI AG
2025-04-01
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| author | Yan Zhang Bingchen Zhang Yirong Wu |
| author_facet | Yan Zhang Bingchen Zhang Yirong Wu |
| author_sort | Yan Zhang |
| collection | DOAJ |
| description | In recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularization methods have the potential to enhance image quality. Nevertheless, conventional unweighted <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="script">l</mi><mn>1</mn></msub></semantics></math></inline-formula> regularization methods struggle to address cases with radar cross section (RCS) distributed over a wide dynamic range, often resulting in insufficient sidelobe suppression, amplitude distortion, and inconsistent super-resolution performance. In this paper, we propose a novel reweighted regularization method, termed multi-segment-reweighted regularization (MSR), for automotive SAR image restoration. By introducing a novel weighting scheme, MSR localizes the global scattering point enhancement problem to the mainlobe scale, effectively mitigating sidelobe interference. This localization ensures consistent enhancement capability independent of RCS variations. Furthermore, MSR employs multi-segment regularization to constrain amplitude within the mainlobes, preserving the characteristics of the original response. Correspondingly, a new thresholding function, named Thinner Response Undistorted THresholding (TRUTH), is introduced. An iterative algorithm for enhancing automotive SAR images using MSR is also presented. Real data experiments validate the feasibility and effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-bee075bf9885410b9b9cbf596bfabf84 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-bee075bf9885410b9b9cbf596bfabf842025-08-20T03:52:57ZengMDPI AGRemote Sensing2072-42922025-04-01179148310.3390/rs17091483Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted RegularizationYan Zhang0Bingchen Zhang1Yirong Wu2Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, ChinaIn recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularization methods have the potential to enhance image quality. Nevertheless, conventional unweighted <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi mathvariant="script">l</mi><mn>1</mn></msub></semantics></math></inline-formula> regularization methods struggle to address cases with radar cross section (RCS) distributed over a wide dynamic range, often resulting in insufficient sidelobe suppression, amplitude distortion, and inconsistent super-resolution performance. In this paper, we propose a novel reweighted regularization method, termed multi-segment-reweighted regularization (MSR), for automotive SAR image restoration. By introducing a novel weighting scheme, MSR localizes the global scattering point enhancement problem to the mainlobe scale, effectively mitigating sidelobe interference. This localization ensures consistent enhancement capability independent of RCS variations. Furthermore, MSR employs multi-segment regularization to constrain amplitude within the mainlobes, preserving the characteristics of the original response. Correspondingly, a new thresholding function, named Thinner Response Undistorted THresholding (TRUTH), is introduced. An iterative algorithm for enhancing automotive SAR images using MSR is also presented. Real data experiments validate the feasibility and effectiveness of the proposed method.https://www.mdpi.com/2072-4292/17/9/1483automotive SARimage restorationweighting schemethresholding functionconsistent enhancementmulti-segment-reweighted regularization |
| spellingShingle | Yan Zhang Bingchen Zhang Yirong Wu Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization Remote Sensing automotive SAR image restoration weighting scheme thresholding function consistent enhancement multi-segment-reweighted regularization |
| title | Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization |
| title_full | Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization |
| title_fullStr | Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization |
| title_full_unstemmed | Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization |
| title_short | Undistorted and Consistent Enhancement of Automotive SAR Image via Multi-Segment-Reweighted Regularization |
| title_sort | undistorted and consistent enhancement of automotive sar image via multi segment reweighted regularization |
| topic | automotive SAR image restoration weighting scheme thresholding function consistent enhancement multi-segment-reweighted regularization |
| url | https://www.mdpi.com/2072-4292/17/9/1483 |
| work_keys_str_mv | AT yanzhang undistortedandconsistentenhancementofautomotivesarimageviamultisegmentreweightedregularization AT bingchenzhang undistortedandconsistentenhancementofautomotivesarimageviamultisegmentreweightedregularization AT yirongwu undistortedandconsistentenhancementofautomotivesarimageviamultisegmentreweightedregularization |