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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.21657 |
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| _version_ | 1866918132645363712 |
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| author | Zhang, Haomiao Li, Zhangyuan Piao, Yanling Li, Zhi Wang, Xiaodong Cao, Miao Su, Xiongfei Song, Qiang Yuan, Xin |
| author_facet | Zhang, Haomiao Li, Zhangyuan Piao, Yanling Li, Zhi Wang, Xiaodong Cao, Miao Su, Xiongfei Song, Qiang Yuan, Xin |
| contents | Computer-generated holography (CGH) has gained wide attention with deep learning-based algorithms. However, due to its nonlinear and ill-posed nature, challenges remain in achieving accurate and stable reconstruction. Specifically, ($i$) the widely used end-to-end networks treat the reconstruction model as a black box, ignoring underlying physical relationships, which reduces interpretability and flexibility. ($ii$) CNN-based CGH algorithms have limited receptive fields, hindering their ability to capture long-range dependencies and global context. ($iii$) Angular spectrum method (ASM)-based models are constrained to finite near-fields.In this paper, we propose a Deep Unfolding Network (DUN) that decomposes gradient descent into two modules: an adaptive bandwidth-preserving model (ABPM) and a phase-domain complex-valued denoiser (PCD), providing more flexibility. ABPM allows for wider working distances compared to ASM-based methods. At the same time, PCD leverages its complex-valued deformable self-attention module to capture global features and enhance performance, achieving a PSNR over 35 dB. Experiments on simulated and real data show state-of-the-art results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unfolding Framework with Complex-Valued Deformable Attention for High-Quality Computer-Generated Hologram Generation Zhang, Haomiao Li, Zhangyuan Piao, Yanling Li, Zhi Wang, Xiaodong Cao, Miao Su, Xiongfei Song, Qiang Yuan, Xin Computer Vision and Pattern Recognition Computer-generated holography (CGH) has gained wide attention with deep learning-based algorithms. However, due to its nonlinear and ill-posed nature, challenges remain in achieving accurate and stable reconstruction. Specifically, ($i$) the widely used end-to-end networks treat the reconstruction model as a black box, ignoring underlying physical relationships, which reduces interpretability and flexibility. ($ii$) CNN-based CGH algorithms have limited receptive fields, hindering their ability to capture long-range dependencies and global context. ($iii$) Angular spectrum method (ASM)-based models are constrained to finite near-fields.In this paper, we propose a Deep Unfolding Network (DUN) that decomposes gradient descent into two modules: an adaptive bandwidth-preserving model (ABPM) and a phase-domain complex-valued denoiser (PCD), providing more flexibility. ABPM allows for wider working distances compared to ASM-based methods. At the same time, PCD leverages its complex-valued deformable self-attention module to capture global features and enhance performance, achieving a PSNR over 35 dB. Experiments on simulated and real data show state-of-the-art results. |
| title | Unfolding Framework with Complex-Valued Deformable Attention for High-Quality Computer-Generated Hologram Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.21657 |