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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.00032 |
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| _version_ | 1866911744124780544 |
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| author | Du, Long-Kun Hu, Chenyu Liu, Shuang Deng, Chenjin Wang, Chaoran Bo, Zunwang Chen, Mingliang Liu, Wei-Tao Han, Shensheng |
| author_facet | Du, Long-Kun Hu, Chenyu Liu, Shuang Deng, Chenjin Wang, Chaoran Bo, Zunwang Chen, Mingliang Liu, Wei-Tao Han, Shensheng |
| contents | Computational imaging~(CI) has been attracting a lot of interest in recent years for its superiority over traditional imaging in various applications. In CI systems, information is generally acquired in an encoded form and subsequently decoded via processing algorithms, which is quite in line with the information transmission mode of modern communication, and leads to emerging studies from the viewpoint of information optical imaging. Currently, one of the most important issues to be theoretically studied for CI is to quantitatively evaluate the fundamental ability of information acquisition, which is essential for both objective performance assessment and efficient design of imaging system. In this paper, by incorporating the Bayesian filtering paradigm, we propose a framework for CI that enables quantitative evaluation and design of the imaging system, and demonstate it based on ghost imaging. In specific, this framework can provide a quantitative evaluation on the acquired information through Fisher information and Cramér-Rao Lower Bound (CRLB), and the intrinsic performance of the imaging system can be accessed in real-time. With simulation and experiments, the framework is validated and compared with existing linear unbiased algorithms. In particular, the image retrieval can reach the CRLB. Furthermore, information-driven adaptive design for optimizing the information acquisition procedure is also achieved. By quantitative describing and efficient designing, the proposed framework is expected to promote the practical applications of CI techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00032 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Bayesian Recursive Information Optical Imaging: A Ghost Imaging Scheme Based on Bayesian Filtering Du, Long-Kun Hu, Chenyu Liu, Shuang Deng, Chenjin Wang, Chaoran Bo, Zunwang Chen, Mingliang Liu, Wei-Tao Han, Shensheng Optics Computational imaging~(CI) has been attracting a lot of interest in recent years for its superiority over traditional imaging in various applications. In CI systems, information is generally acquired in an encoded form and subsequently decoded via processing algorithms, which is quite in line with the information transmission mode of modern communication, and leads to emerging studies from the viewpoint of information optical imaging. Currently, one of the most important issues to be theoretically studied for CI is to quantitatively evaluate the fundamental ability of information acquisition, which is essential for both objective performance assessment and efficient design of imaging system. In this paper, by incorporating the Bayesian filtering paradigm, we propose a framework for CI that enables quantitative evaluation and design of the imaging system, and demonstate it based on ghost imaging. In specific, this framework can provide a quantitative evaluation on the acquired information through Fisher information and Cramér-Rao Lower Bound (CRLB), and the intrinsic performance of the imaging system can be accessed in real-time. With simulation and experiments, the framework is validated and compared with existing linear unbiased algorithms. In particular, the image retrieval can reach the CRLB. Furthermore, information-driven adaptive design for optimizing the information acquisition procedure is also achieved. By quantitative describing and efficient designing, the proposed framework is expected to promote the practical applications of CI techniques. |
| title | Bayesian Recursive Information Optical Imaging: A Ghost Imaging Scheme Based on Bayesian Filtering |
| topic | Optics |
| url | https://arxiv.org/abs/2401.00032 |