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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/2509.17427 |
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| _version_ | 1866916971163942912 |
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| author | Kawachi, Hodaka Neto, Jose Reinaldo Cunha Santos A. V. Silva Yagi, Yasushi Nagahara, Hajime Nakamura, Tomoya |
| author_facet | Kawachi, Hodaka Neto, Jose Reinaldo Cunha Santos A. V. Silva Yagi, Yasushi Nagahara, Hajime Nakamura, Tomoya |
| contents | We propose a single-snapshot depth-from-defocus (DFD) reconstruction method for coded-aperture imaging that replaces hand-crafted priors with a learned diffusion prior used purely as regularization. Our optimization framework enforces measurement consistency via a differentiable forward model while guiding solutions with the diffusion prior in the denoised image domain, yielding higher accuracy and stability than classical optimization. Unlike U-Net-style regressors, our approach requires no paired defocus-RGBD training data and does not tie training to a specific camera configuration. Experiments on comprehensive simulations and a prototype camera demonstrate consistently strong RGBD reconstructions across noise levels, outperforming both U-Net baselines and a classical coded-aperture DFD method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Single-Image Depth from Defocus with Coded Aperture and Diffusion Posterior Sampling Kawachi, Hodaka Neto, Jose Reinaldo Cunha Santos A. V. Silva Yagi, Yasushi Nagahara, Hajime Nakamura, Tomoya Computer Vision and Pattern Recognition We propose a single-snapshot depth-from-defocus (DFD) reconstruction method for coded-aperture imaging that replaces hand-crafted priors with a learned diffusion prior used purely as regularization. Our optimization framework enforces measurement consistency via a differentiable forward model while guiding solutions with the diffusion prior in the denoised image domain, yielding higher accuracy and stability than classical optimization. Unlike U-Net-style regressors, our approach requires no paired defocus-RGBD training data and does not tie training to a specific camera configuration. Experiments on comprehensive simulations and a prototype camera demonstrate consistently strong RGBD reconstructions across noise levels, outperforming both U-Net baselines and a classical coded-aperture DFD method. |
| title | Single-Image Depth from Defocus with Coded Aperture and Diffusion Posterior Sampling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.17427 |