Hyperspectral Computational Ghost Imaging via Temporal Multiplexing

Abstract

Computational ghost imaging has made large progress in both spatial resolution and acquisition efficiency, but so far still cannot resolve the spectral reflectance well. This letter proposes a spectrum encoded acquisition scheme to achieve computational ghost imaging of hyperspectral data. Taking advantage of the speed gap between the extremely fast response of the bucket detector and magnitudes lower spatial illumination modulation, our approach temporally multiplexes a group of diverse spectra into the elapse of each 2-D illumination pattern. The number and the type of the multiplexed spectra are optimized utilizing the low intrinsic dimension of the hyperspectral data and based on the reconstruction quality on a diverse range of nature materials. After data acquisition, we infer the top principal component analysis projections of the hyperspectral image from the demultiplexed correlated measurements and the illumination patterns, and then reconstruct the final hyperspectral data. As far as we know, we are the first one to achieve computational hyperspectral ghost imaging with high accuracy in a few seconds.