Physics-constrained deep-inverse point spread function model: toward non-line-of-sight imaging reconstruction Article Swipe
Wu Su
,
Chan Huang
,
Jing Lin
,
Tao Wang
,
Shanshan Zheng
,
Haisheng Feng
,
Lei Yu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1117/1.apn.3.2.026010
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1117/1.apn.3.2.026010
Non-line-of-sight (NLOS) imaging has emerged as a prominent technique for reconstructing obscured objects from images that undergo multiple diffuse reflections. This imaging method has garnered significant attention in diverse domains, including remote sensing, rescue operations, and intelligent driving, due to its wide-ranging potential applications. Nevertheless, accurately modeling the incident light direction, which carries energy and is captured by the detector amidst random diffuse reflection directions, poses a considerable challenge. This challenge hinders the acquisition of precise forward and inverse physical models for NLOS imaging, which are crucial for achieving high-quality reconstructions. In this study, we propose a point spread function (PSF) model for the NLOS imaging system utilizing ray tracing with random angles. Furthermore, we introduce a reconstruction method, termed the physics-constrained inverse network (PCIN), which establishes an accurate PSF model and inverse physical model by leveraging the interplay between PSF constraints and the optimization of a convolutional neural network. The PCIN approach initializes the parameters randomly, guided by the constraints of the forward PSF model, thereby obviating the need for extensive training data sets, as required by traditional deep-learning methods. Through alternating iteration and gradient descent algorithms, we iteratively optimize the diffuse reflection angles in the PSF model and the neural network parameters. The results demonstrate that PCIN achieves efficient data utilization by not necessitating a large number of actual ground data groups. Moreover, the experimental findings confirm that the proposed method effectively restores the hidden object features with high accuracy.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1117/1.apn.3.2.026010
- https://www.spiedigitallibrary.org/journals/advanced-photonics-nexus/volume-3/issue-2/026010/Physics-constrained-deep-inverse-point-spread-function-model--toward/10.1117/1.APN.3.2.026010.pdf
- OA Status
- diamond
- Cited By
- 6
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392910234
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- OpenAlex ID
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https://openalex.org/W4392910234Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1117/1.apn.3.2.026010Digital Object Identifier
- Title
-
Physics-constrained deep-inverse point spread function model: toward non-line-of-sight imaging reconstructionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-18Full publication date if available
- Authors
-
Wu Su, Chan Huang, Jing Lin, Tao Wang, Shanshan Zheng, Haisheng Feng, Lei YuList of authors in order
- Landing page
-
https://doi.org/10.1117/1.apn.3.2.026010Publisher landing page
- PDF URL
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https://www.spiedigitallibrary.org/journals/advanced-photonics-nexus/volume-3/issue-2/026010/Physics-constrained-deep-inverse-point-spread-function-model--toward/10.1117/1.APN.3.2.026010.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.spiedigitallibrary.org/journals/advanced-photonics-nexus/volume-3/issue-2/026010/Physics-constrained-deep-inverse-point-spread-function-model--toward/10.1117/1.APN.3.2.026010.pdfDirect OA link when available
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Line-of-sight, Physics, Inverse problem, Point spread function, Line (geometry), Point (geometry), Function (biology), Inverse, Statistical physics, Optics, Astrophysics, Mathematical analysis, Geometry, Mathematics, Biology, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 5, 2024: 1Per-year citation counts (last 5 years)
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51Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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