Abstract:
Massive amounts of data in spectral imagery increase acquisition, storing, and processing costs. Compressive spectral imaging (CSI) methods allow the reconstruction of sp...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Image Processing ( Volume: 27, Issue: 12, December 2018)