Phase Retrieval Based on DC and DnCNN
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866915629691305984 |
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| author | Li, Xueming Guo, Bing |
| author_facet | Li, Xueming Guo, Bing |
| contents | This paper investigates noise-robust phase retrieval by enhancing the prDeep architecture with difference of convex functions (DC) and DnCNN-based denoising regularization. This research introduces two novel algorithms, prDeep-DC and prDeep-L2, which demonstrably achieve excellent quantitative and visual performance, as confirmed by extensive numerical experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase Retrieval Based on DC and DnCNN Li, Xueming Guo, Bing Optimization and Control This paper investigates noise-robust phase retrieval by enhancing the prDeep architecture with difference of convex functions (DC) and DnCNN-based denoising regularization. This research introduces two novel algorithms, prDeep-DC and prDeep-L2, which demonstrably achieve excellent quantitative and visual performance, as confirmed by extensive numerical experiments. |
| title | Phase Retrieval Based on DC and DnCNN |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2511.16913 |