Horisaki Ryoichi, Tanida Jun
Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, -5 Yamadaoka, Suita, Osaka 565-0871, Japan.
Opt Express. 2010 Oct 25;18(22):23041-53. doi: 10.1364/OE.18.023041.
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.
本文描述了一种用于多路复用空间编码成像系统以获取多通道数据的广义理论框架。该框架通过模拟和实验演示得到了验证。在该系统中，与物体相关的每个通道都进行了空间编码，并且所得信号被多路复用到一个探测器阵列上。在解复用过程中，使用一种具有稀疏性约束的数值估计算法来解决欠定重建问题。该系统可以获取元素数量大于所采集数据数量的物体数据。这种情况包括使用探测器阵列单次采集多通道数据。在实验中，对稀疏物体进行了宽视场成像和光谱成像演示。一种名为具有总变分的两步迭代收缩/阈值算法的压缩感知算法被用于物体重建。
