Baranski Maciej, Rehman Shakil, Muttikulangara Sanathanan S, Barbastathis George, Miao Jianmin
J Opt Soc Am A Opt Image Sci Vis. 2017 Sep 1;34(9):1711-1719. doi: 10.1364/JOSAA.34.001711.
Integral field spectroscopy (IFS) is a well-established method for measuring spectral intensity data of the form s(x,y,λ), where x, y are spatial coordinates and λ is the wavelength. In most flavors of IFS, there is a trade-off between sampling (x,y) and the measured wavelength band Δλ. Here we present the first, to our knowledge, attempt to overcome this trade-off by use of computational imaging and measurement diversity. We implement diversity by including a grating in our design, which allows rotation of the dispersed spectra between measurements. The raw intensity data captured from the rotated grating positions are then processed by an inverse algorithm that utilizes sparsity in the data. We present simulated results from spatial-spectral data in the experimental dataset. We used non-overlapping portions of the dataset to train our sparsity priors in the form of the dictionary, and to test the reconstruction quality. We found that, depending on the level of noise in the measurement, diversity up to a maximum number of measurements is beneficial in terms of reducing error, and yields diminishing returns if even more measurements are taken.
积分场光谱学（IFS）是一种成熟的方法，用于测量形如s(x,y,λ)的光谱强度数据，其中x、y是空间坐标，λ是波长。在大多数IFS类型中，在采样(x,y)和测量的波长范围Δλ之间存在权衡。据我们所知，这里我们首次尝试通过使用计算成像和测量多样性来克服这种权衡。我们通过在设计中加入一个光栅来实现多样性，这使得在测量之间可以旋转色散光谱。然后，从旋转光栅位置捕获的原始强度数据由利用数据稀疏性的逆算法进行处理。我们展示了实验数据集中空间光谱数据的模拟结果。我们使用数据集的非重叠部分来训练以字典形式表示的稀疏先验，并测试重建质量。我们发现，根据测量中的噪声水平，在达到最大测量次数之前增加多样性有利于减少误差，但如果进行更多测量，收益会递减。
