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| Auteurs principaux: | , , , |
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| Format: | Preprint |
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2026
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| Accès en ligne: | https://arxiv.org/abs/2604.26664 |
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| _version_ | 1866911632807952384 |
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| author | Liu, Carson Yu Cheng, Jun Chen, Chien-Chun Shu, Steve F. |
| author_facet | Liu, Carson Yu Cheng, Jun Chen, Chien-Chun Shu, Steve F. |
| contents | Traditional iterative reconstruction methods are accurate but computationally expensive, limiting their use in high-throughput and real-time ptychography. Recent deep learning approaches improve speed, but often predict phase as a Euclidean scalar despite its $2π$ periodicity, which can introduce wrapping artifacts, discontinuities at $\pmπ$, and a mismatch between the loss and the underlying signal geometry. We present a deep learning framework for ptychographic reconstruction that models phase on the unit circle using cosine and sine components. Phase error is optimized with a differentiable geodesic loss, which avoids branch-cut discontinuities and provides bounded gradients. The network further incorporates saturation-aware dual-gain input scaling, parallel encoder branches, and three decoders for amplitude, cosine, and sine prediction, together with a composite loss that promotes circular consistency and structural fidelity. Experiments on synthetic and experimental datasets show consistent improvements in both amplitude and phase reconstruction over existing deep learning methods. Frequency-domain analysis further shows better preservation of mid- and high-frequency phase content. The proposed method also provides substantial speedup over iterative solvers while maintaining physically consistent reconstructions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26664 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction Liu, Carson Yu Cheng, Jun Chen, Chien-Chun Shu, Steve F. Image and Video Processing Computer Vision and Pattern Recognition Optics Traditional iterative reconstruction methods are accurate but computationally expensive, limiting their use in high-throughput and real-time ptychography. Recent deep learning approaches improve speed, but often predict phase as a Euclidean scalar despite its $2π$ periodicity, which can introduce wrapping artifacts, discontinuities at $\pmπ$, and a mismatch between the loss and the underlying signal geometry. We present a deep learning framework for ptychographic reconstruction that models phase on the unit circle using cosine and sine components. Phase error is optimized with a differentiable geodesic loss, which avoids branch-cut discontinuities and provides bounded gradients. The network further incorporates saturation-aware dual-gain input scaling, parallel encoder branches, and three decoders for amplitude, cosine, and sine prediction, together with a composite loss that promotes circular consistency and structural fidelity. Experiments on synthetic and experimental datasets show consistent improvements in both amplitude and phase reconstruction over existing deep learning methods. Frequency-domain analysis further shows better preservation of mid- and high-frequency phase content. The proposed method also provides substantial speedup over iterative solvers while maintaining physically consistent reconstructions. |
| title | Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2604.26664 |