{"ID":2838411,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16913","arxiv_id":"2511.16913","title":"Phase Retrieval Based on DC and DnCNN","abstract":"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.","short_abstract":"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, a...","url_abs":"https://arxiv.org/abs/2511.16913","url_pdf":"https://arxiv.org/pdf/2511.16913v1","authors":"[\"Xueming Li\",\"Bing Guo\"]","published":"2025-11-21T02:51:33Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}