Chen, Che ; Chen, Lin ; Jiang, Xue ; Liu, Xingzhao ; Zoubir, Abdelhak M. (2024)
Deep Unrolling Network for SAR Image Despeckling.
49th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024). Seoul, Republic of Korea (14.-19.04.2024)
doi: 10.1109/ICASSP48485.2024.10447792
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Synthetic aperture radar (SAR) images are inherently affected by speckle noise. Deep learning-based methods have shown good potential in image denoising task. Most deep learning methods for denoising focus on additive Gaussian noise removal. However, SAR images are usually contaminated by non-Gaussian multiplicative speckle noise. In this paper, we propose a novel deep unrolling network named SAR-DURNet to deal with the SAR image despeckling problem. We establish optimization problem of speckle noise removal by using the priori of noise distribution, which can be sovled by half-quadratic splitting (HQS) method with iterative steps. We unroll the iterative process into a trainable deep unrolling network(SAR-DURNet). The parameters of the SAR-DURNet are trained end-to-end with simulated SAR image dataset. Experimental results on simulated test data and real SAR data show that the proposed approach has superior results in terms of quantitative performance metrics and the preservation of intricate visual details, compared to several well-known SAR image despeckling methods.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Chen, Che ; Chen, Lin ; Jiang, Xue ; Liu, Xingzhao ; Zoubir, Abdelhak M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Deep Unrolling Network for SAR Image Despeckling |
| Sprache: | Englisch |
| Publikationsjahr: | 18 März 2024 |
| Verlag: | IEEE |
| Buchtitel: | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing: Proceedings |
| Veranstaltungstitel: | 49th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024) |
| Veranstaltungsort: | Seoul, Republic of Korea |
| Veranstaltungsdatum: | 14.-19.04.2024 |
| DOI: | 10.1109/ICASSP48485.2024.10447792 |
| Kurzbeschreibung (Abstract): | Synthetic aperture radar (SAR) images are inherently affected by speckle noise. Deep learning-based methods have shown good potential in image denoising task. Most deep learning methods for denoising focus on additive Gaussian noise removal. However, SAR images are usually contaminated by non-Gaussian multiplicative speckle noise. In this paper, we propose a novel deep unrolling network named SAR-DURNet to deal with the SAR image despeckling problem. We establish optimization problem of speckle noise removal by using the priori of noise distribution, which can be sovled by half-quadratic splitting (HQS) method with iterative steps. We unroll the iterative process into a trainable deep unrolling network(SAR-DURNet). The parameters of the SAR-DURNet are trained end-to-end with simulated SAR image dataset. Experimental results on simulated test data and real SAR data show that the proposed approach has superior results in terms of quantitative performance metrics and the preservation of intricate visual details, compared to several well-known SAR image despeckling methods. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung |
| Hinterlegungsdatum: | 04 Apr 2024 11:52 |
| Letzte Änderung: | 16 Apr 2024 07:03 |
| PPN: | 517168928 |
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