Multi-Resolution Compressive Spectral Imaging Reconstruction From Single Pixel Measurements

Massive amounts of data in spectral imagery increase acquisition, storing, and processing costs. Compressive spectral imaging (CSI) methods allow the reconstruction of spatial and spectral information from a small set of random projections. The single pixel camera is a low-cost optical architecture, which enables the compressive acquisition of spectral images. Traditional CSI reconstruction methods obtain a sparse approximation of the underlying spatial and spectral information; however, the complexity of these algorithms increases in proportion to the dimensionality of the data. This paper proposes a multi-resolution (MR) CSI reconstruction approach from single-pixel camera measurements that exploits spectral similarities between the pixels to group them in super-pixels, such that the total number of unknowns in the inverse problem is reduced. Specifically, two different types of super-pixels are considered: rectangular and irregular structures. Simulation and experimental results show that the proposed MR scheme improves the reconstruction quality in up to 6 dB of peak signal-to-noise ratio and reconstruction time in up to 90% with respect to the traditional full resolution reconstructions.