Gedalin Daniel, Oiknine Yaniv, Stern Adrian
Opt Express. 2019 Nov 25;27(24):35811-35822. doi: 10.1364/OE.27.035811.
Several hyperspectral (HS) systems based on compressive sensing (CS) theory have been presented to capture HS images with high accuracy and with a lower number of measurements than needed by conventional systems. However, the reconstruction of HS compressed measurements is time-consuming and commonly involves hyperparameter tuning per each scenario. In this paper, we introduce a Convolutional Neural Network (CNN) designed for the reconstruction of HS cubes captured with CS imagers based on spectral modulation. Our Deep Neural Network (DNN), dubbed DeepCubeNet, provides significant reduction in the reconstruction time compared to classical iterative methods. The performance of DeepCubeNet is investigated on simulated data, and we demonstrate for the first time, to the best of our knowledge, real reconstruction of CS HS measurements using DNN. We demonstrate significantly enhanced reconstruction accuracy compared to iterative CS reconstruction, as well as improvement in reconstruction time by many orders of magnitude.
已经提出了几种基于压缩感知（CS）理论的高光谱（HS）系统，以高精度捕获HS图像，并且所需的测量次数比传统系统少。然而，HS压缩测量的重建很耗时，并且通常在每种情况下都涉及超参数调整。在本文中，我们介绍了一种卷积神经网络（CNN），该网络专为基于光谱调制的CS成像仪捕获的HS立方体的重建而设计。我们的深度神经网络（DNN），称为DeepCubeNet，与经典迭代方法相比，重建时间显著减少。在模拟数据上研究了DeepCubeNet的性能，并且据我们所知，我们首次展示了使用DNN对CS HS测量进行实际重建。与迭代CS重建相比，我们展示了显著提高的重建精度，以及重建时间提高了多个数量级。
