Spectral Polarization Image Reconstruction Using Compressed Sensing Method
Ying Zhang, Heshen Li, Junhua Sun, Xi Zhang, Hao Wang
Abstract
The coded aperture snapshot spectral imager (CASSI) system measures multidimensional signals by compressing the original signal into a 2D snapshot, and then reconstructs the original signal by employing certain reconstruction algorithms. In recent years, the compressed sensing (CS) algorithm has been used to solve the inverse problems in the CASSI system. However, the practical application of the CS algorithm requires a balance among accuracy, robustness, speed, and stability. In this research, the CS algorithm is combined with a CASSI system with a division of focal plane polarization detector to reconstruct spatial, spectral and polarization information at the same time. The reconstruction results correspond to the reference data, and the experimental results show that the targets can be distinguished by spectral and polarization information. Moreover, in the scenes characterized by significant variations in light intensity, targets can be distinguished from the background through polarization imaging detection, indicating its advantages under certain circumstances. Inspired by genetic algorithms, a method for optimizing the coded aperture targeting certain sparse basis is also proposed. The results demonstrate that the correlation between the observation matrix and the sparse basis has decreased by 42.7%, leading to the improvement of the reconstruction effect of the spectral curves.