A Micro-Mirror Array based System for Compressive Sensing of Hyperspectral Data (original) (raw)

We introduce a system for hyperspectral imaging, which includes a micro-mirror array that projects subsets of image pixels onto a prism (or diffraction grating), followed by a CCD-type sensor. This system allows generalized sampling schemes termed as Compressed Sensing (CS). We acquire only a fraction of the samples that are required to obtain the full-resolution signal (hyperspectral cube in our case), and by means of non-linear optimization recover the underlying signal. We use a prior knowledge about the signal sparsity in some fixed dictionary, and also its limited total variation. In the practical setting developed here, the feasible sampling is not ideal for CS due to practical limitations, and the sensed signal does not necessarily meet the strict sparsity demands of CS theory. Therefore we introduce additional measurements of full-resolution image using small number of filters similar to RGB. As a result, we obtain a feasible system for hyperspectral imaging that enables faster acquisition compared to traditional sampling systems.