Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.16913 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915629691305984 |
|---|---|
| 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 |