Multi-channel data acquisition using multiplexed imaging with spatial encoding
This paper describes a generalized theoretical framework for a multiplexed spatially encoded imaging system to acquire multi-channel data. The framework is confirmed with simulations and experimental demonstrations. In the system, each channel associated with the object is spatially encoded, and the...
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| Veröffentlicht in: | Optics express Jg. 18; H. 22; S. 23041 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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25.10.2010
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| ISSN: | 1094-4087, 1094-4087 |
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| Abstract | This paper describes a generalized theoretical framework for a multiplexed spatially encoded imaging system to acquire multi-channel data. The framework is confirmed with simulations and experimental demonstrations. In the system, each channel associated with the object is spatially encoded, and the resultant signals are multiplexed onto a detector array. In the demultiplexing process, a numerical estimation algorithm with a sparsity constraint is used to solve the underdetermined reconstruction problem. The system can acquire object data in which the number of elements is larger than that of the captured data. This case includes multi-channel data acquisition by a single-shot with a detector array. In the experiments, wide field-of-view imaging and spectral imaging were demonstrated with sparse objects. A compressive sensing algorithm, called the two-step iterative shrinkage/thresholding algorithm with total variation, was adapted for object reconstruction. |
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| AbstractList | This paper describes a generalized theoretical framework for a multiplexed spatially encoded imaging system to acquire multi-channel data. The framework is confirmed with simulations and experimental demonstrations. In the system, each channel associated with the object is spatially encoded, and the resultant signals are multiplexed onto a detector array. In the demultiplexing process, a numerical estimation algorithm with a sparsity constraint is used to solve the underdetermined reconstruction problem. The system can acquire object data in which the number of elements is larger than that of the captured data. This case includes multi-channel data acquisition by a single-shot with a detector array. In the experiments, wide field-of-view imaging and spectral imaging were demonstrated with sparse objects. A compressive sensing algorithm, called the two-step iterative shrinkage/thresholding algorithm with total variation, was adapted for object reconstruction.This paper describes a generalized theoretical framework for a multiplexed spatially encoded imaging system to acquire multi-channel data. The framework is confirmed with simulations and experimental demonstrations. In the system, each channel associated with the object is spatially encoded, and the resultant signals are multiplexed onto a detector array. In the demultiplexing process, a numerical estimation algorithm with a sparsity constraint is used to solve the underdetermined reconstruction problem. The system can acquire object data in which the number of elements is larger than that of the captured data. This case includes multi-channel data acquisition by a single-shot with a detector array. In the experiments, wide field-of-view imaging and spectral imaging were demonstrated with sparse objects. A compressive sensing algorithm, called the two-step iterative shrinkage/thresholding algorithm with total variation, was adapted for object reconstruction. This paper describes a generalized theoretical framework for a multiplexed spatially encoded imaging system to acquire multi-channel data. The framework is confirmed with simulations and experimental demonstrations. In the system, each channel associated with the object is spatially encoded, and the resultant signals are multiplexed onto a detector array. In the demultiplexing process, a numerical estimation algorithm with a sparsity constraint is used to solve the underdetermined reconstruction problem. The system can acquire object data in which the number of elements is larger than that of the captured data. This case includes multi-channel data acquisition by a single-shot with a detector array. In the experiments, wide field-of-view imaging and spectral imaging were demonstrated with sparse objects. A compressive sensing algorithm, called the two-step iterative shrinkage/thresholding algorithm with total variation, was adapted for object reconstruction. |
| Author | Tanida, Jun Horisaki, Ryoichi |
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| Cites_doi | 10.1364/OE.17.013040 10.1109/TIT.2003.820031 10.1109/34.625126 10.1364/OE.15.014013 10.1109/TIP.2007.909319 10.1364/OE.18.019367 10.1364/JOSA.62.000055 10.1109/MSP.2007.914731 10.1109/TIT.2005.858979 10.1109/MSP.2007.4286571 10.1364/OE.16.016352 10.1109/TIT.2006.871582 10.1364/AO.47.000B44 10.1086/111605 10.1109/MC.2006.270 10.1016/0167-2789(92)90242-F 10.1364/AO.34.001859 |
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