García-Sánchez Ignacio, Fresnedo Óscar, González-Coma José P, Castedo Luis
Department of Computer Engineering & CITIC Research Center, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain.
Defense University Center, The Spanish Naval Academy, University of Vigo, Plaza de España 2, Marín, 36920 Pontevedra, Spain.
Sensors (Basel). 2021 Sep 30;21(19):6551. doi: 10.3390/s21196551.
In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.
在这项工作中，我们研究并分析了用CASSI设备采样的高光谱图像的重建。借助压缩感知（CS）理论对传感过程进行建模，该理论为从压缩测量中重建高光谱图像提供了高效机制。具体而言，我们考虑并比较了四种不同类型的估计算法：正交匹配追踪（OMP）、梯度投影稀疏重构（GPSR）、最小绝对收缩和选择算子（LASSO）以及迭代软阈值（IST）。此外，高光谱图像的大尺寸要求实现一个实用的块CASSI模型，以便在可接受的延迟和可承受的计算成本下重建图像。为了考虑块模型的特殊性以及类似CASSI传感过程中的色散效应，对问题进行了重新表述，并构建了相关变量。对于这种实用的CASSI设置，我们通过考虑上述算法以及影响重建过程的不同因素来评估整个系统的性能。最后，从实际角度对获得的结果进行了分析和讨论。
