Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors
Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with detectors sensitive to individual photons, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust me...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/111032 https://orcid.org/0000-0001-9289-829X https://orcid.org/0000-0002-6094-5861 |
| _version_ | 1826214464453607424 |
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| author | Kirmani, Ahmed Shin, Dongeek Goyal, Vivek K Shapiro, Jeffrey H |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Kirmani, Ahmed Shin, Dongeek Goyal, Vivek K Shapiro, Jeffrey H |
| author_sort | Kirmani, Ahmed |
| collection | MIT |
| description | Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with detectors sensitive to individual photons, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using fixed dwell time per pixel and on the order of one detected photon per pixel averaged over the scene. Our computational image formation method combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3-D structure. Experiments conducted in the presence of strong background light demonstrate that our method is able to accurately recover scene depth and reflectivity, while traditional imaging methods based on maximum likelihood (ML) estimation or approximations thereof lead to noisier images. For depth, performance compares favorably to signal-independent noise removal algorithms such as median filtering or block-matching and 3-D filtering (BM3D) applied to the pixelwise ML estimate; for reflectivity, performance is similar to signal-dependent noise removal algorithms such as Poisson nonlocal sparse PCA and BM3D with variance-stabilizing transformation. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus, our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array. |
| first_indexed | 2024-09-23T16:06:09Z |
| format | Article |
| id | mit-1721.1/111032 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:06:09Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1110322024-06-28T14:28:52Z Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors Kirmani, Ahmed Shin, Dongeek Goyal, Vivek K Shapiro, Jeffrey H Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Shin, Dongeek Goyal, Vivek K Shapiro, Jeffrey H Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with detectors sensitive to individual photons, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using fixed dwell time per pixel and on the order of one detected photon per pixel averaged over the scene. Our computational image formation method combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3-D structure. Experiments conducted in the presence of strong background light demonstrate that our method is able to accurately recover scene depth and reflectivity, while traditional imaging methods based on maximum likelihood (ML) estimation or approximations thereof lead to noisier images. For depth, performance compares favorably to signal-independent noise removal algorithms such as median filtering or block-matching and 3-D filtering (BM3D) applied to the pixelwise ML estimate; for reflectivity, performance is similar to signal-dependent noise removal algorithms such as Poisson nonlocal sparse PCA and BM3D with variance-stabilizing transformation. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus, our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array. 2017-08-28T18:35:00Z 2017-08-28T18:35:00Z 2015-07 Article http://purl.org/eprint/type/JournalArticle 2333-9403 2334-0118 http://hdl.handle.net/1721.1/111032 Shin, Dongeek, et al. “Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors.” IEEE Transactions on Computational Imaging 1, 2 (June 2015): 112–125 © 2015 Institute of Electrical and Electronics Engineers (IEEE) https://orcid.org/0000-0001-9289-829X https://orcid.org/0000-0002-6094-5861 en_US http://dx.doi.org/10.1109/TCI.2015.2453093 IEEE Transactions on Computational Imaging Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Kirmani, Ahmed Shin, Dongeek Goyal, Vivek K Shapiro, Jeffrey H Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors |
| title | Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors |
| title_full | Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors |
| title_fullStr | Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors |
| title_full_unstemmed | Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors |
| title_short | Photon-Efficient Computational 3-D and Reflectivity Imaging With Single-Photon Detectors |
| title_sort | photon efficient computational 3 d and reflectivity imaging with single photon detectors |
| url | http://hdl.handle.net/1721.1/111032 https://orcid.org/0000-0001-9289-829X https://orcid.org/0000-0002-6094-5861 |
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