Rueda Hoover, Lau Daniel, Arce Gonzalo R
Opt Express. 2015 May 4;23(9):12207-21. doi: 10.1364/OE.23.012207.
Compressive sensing is a powerful sensing and reconstruction framework for recovering high dimensional signals with only a handful of observations and for spectral imaging, compressive sensing offers a novel method of multispectral imaging. Specifically, the coded aperture snapshot spectral imager (CASSI) system has been demonstrated to produce multi-spectral data cubes color images from a single snapshot taken by a monochrome image sensor. In this paper, we expand the theoretical framework of CASSI to include the spectral sensitivity of the image sensor pixels to account for color and then investigate the impact on image quality using either a traditional color image sensor that spatially multiplexes red, green, and blue light filters or a novel Foveon image sensor which stacks red, green, and blue pixels on top of one another.
压缩感知是一种强大的传感与重建框架，用于仅通过少量观测来恢复高维信号，并且对于光谱成像而言，压缩感知提供了一种新型多光谱成像方法。具体而言，编码孔径快照光谱成像仪（CASSI）系统已被证明能够从单色图像传感器拍摄的单个快照中生成多光谱数据立方体彩色图像。在本文中，我们扩展了CASSI的理论框架，以纳入图像传感器像素的光谱灵敏度来考虑颜色因素，然后使用在空间上复用红色、绿色和蓝色滤光片的传统彩色图像传感器或一种将红色、绿色和蓝色像素彼此堆叠的新型Foveon图像传感器来研究对图像质量的影响。
