On the use of deep learning for phase recovery
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909057728643072 |
|---|---|
| author | Wang, Kaiqiang Song, Li Wang, Chutian Ren, Zhenbo Zhao, Guangyuan Dou, Jiazhen Di, Jianglei Barbastathis, George Zhou, Renjie Zhao, Jianlin Lam, Edmund Y. |
| author_facet | Wang, Kaiqiang Song, Li Wang, Chutian Ren, Zhenbo Zhao, Guangyuan Dou, Jiazhen Di, Jianglei Barbastathis, George Zhou, Renjie Zhao, Jianlin Lam, Edmund Y. |
| contents | Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_00942 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On the use of deep learning for phase recovery Wang, Kaiqiang Song, Li Wang, Chutian Ren, Zhenbo Zhao, Guangyuan Dou, Jiazhen Di, Jianglei Barbastathis, George Zhou, Renjie Zhao, Jianlin Lam, Edmund Y. Optics Machine Learning Image and Video Processing Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR. |
| title | On the use of deep learning for phase recovery |
| topic | Optics Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2308.00942 |