Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909245219274752 |
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| author | Quero, Carlos Osorio Leykam, Daniel Ojeda, Irving Rondon |
| author_facet | Quero, Carlos Osorio Leykam, Daniel Ojeda, Irving Rondon |
| contents | Conventional deep learning-based image reconstruction methods require a large amount of training data which can be hard to obtain in practice. Untrained deep learning methods overcome this limitation by training a network to invert a physical model of the image formation process. Here we present a novel untrained Res-U2Net model for phase retrieval. We use the extracted phase information to determine changes in an object's surface and generate a mesh representation of its 3D structure. We compare the performance of Res-U2Net phase retrieval against UNet and U2Net using images from the GDXRAY dataset. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_06657 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction Quero, Carlos Osorio Leykam, Daniel Ojeda, Irving Rondon Image and Video Processing Computer Vision and Pattern Recognition Applied Physics Optics Conventional deep learning-based image reconstruction methods require a large amount of training data which can be hard to obtain in practice. Untrained deep learning methods overcome this limitation by training a network to invert a physical model of the image formation process. Here we present a novel untrained Res-U2Net model for phase retrieval. We use the extracted phase information to determine changes in an object's surface and generate a mesh representation of its 3D structure. We compare the performance of Res-U2Net phase retrieval against UNet and U2Net using images from the GDXRAY dataset. |
| title | Res-U2Net: Untrained Deep Learning for Phase Retrieval and Image Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Applied Physics Optics |
| url | https://arxiv.org/abs/2404.06657 |