Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909897423060992 |
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| author | Kim, Jeongsol Lee, Chanseok You, Jongin Ye, Jong Chul Jang, Mooseok |
| author_facet | Kim, Jeongsol Lee, Chanseok You, Jongin Ye, Jong Chul Jang, Mooseok |
| contents | Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a novel off-the-shelf solution that leverages a diffusion model trained solely on object amplitude to recover both amplitude and phase from diffraction intensities. Using a predictor-corrector sampling framework with separate likelihood gradients for amplitude and phase, our method enables complex field reconstruction without requiring ground-truth phase data for training. We validate the proposed approach through extensive simulations and experiments, demonstrating robust generalization across diverse object shapes, imaging system configurations, and modalities, including lensless setups. Notably, a diffusion prior trained on simple amplitude data (e.g., polystyrene beads) successfully reconstructs complex biological tissue structures, highlighting the method's adaptability. This framework provides a cost-effective, generalizable solution for nonlinear inverse problems in computational imaging, and establishes a foundation for broader coherent imaging applications beyond holography. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12728 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors Kim, Jeongsol Lee, Chanseok You, Jongin Ye, Jong Chul Jang, Mooseok Optics Computer Vision and Pattern Recognition Machine Learning Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a novel off-the-shelf solution that leverages a diffusion model trained solely on object amplitude to recover both amplitude and phase from diffraction intensities. Using a predictor-corrector sampling framework with separate likelihood gradients for amplitude and phase, our method enables complex field reconstruction without requiring ground-truth phase data for training. We validate the proposed approach through extensive simulations and experiments, demonstrating robust generalization across diverse object shapes, imaging system configurations, and modalities, including lensless setups. Notably, a diffusion prior trained on simple amplitude data (e.g., polystyrene beads) successfully reconstructs complex biological tissue structures, highlighting the method's adaptability. This framework provides a cost-effective, generalizable solution for nonlinear inverse problems in computational imaging, and establishes a foundation for broader coherent imaging applications beyond holography. |
| title | Generalizable Holographic Reconstruction via Amplitude-Only Diffusion Priors |
| topic | Optics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2509.12728 |