Redundant compressed single-pixel hyperspectral imaging system

Abstract

As single-pixel imaging technology is highly sensitive and low-cost, a large number of hyperspectral imaging methods based on single-pixel have been proposed. Although the image of each band can be reconstructed independently, the parameters that need to be computed for reconstructing hyperspectral images will increase in parallel with the number of bands. Therefore, in this paper, we propose a redundant compressed single-pixel hyperspectral imaging system (RCSHI), which takes full advantage of the spectral redundancy of hyperspectral images by jointly reconstructing images from different bands and compressing the spectral dimension of the data. RCSHI reduces the amount of redundant hyperspectral information stacking and the number of parameters that need to be computed to reconstruct hyperspectral images. In addition, we use progressive fusion enhancement to recover the compressed spectral dimension and enhance the spatial dimension of the images. Experimental results show that RCSHI is able to acquire hyperspectral data with high spatial and spectral resolution and performs well on various datasets and actual scenarios, especially at lower compressed sampling rates.