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 work proposes a multiresolution (MR) CSI reconstruction approach from single pixel camera measurements that exploits spectral similarities between 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 reconstruction quality in up to 6dB of PSNR and reconstruction time in up to 90% with respect to the traditional full resolution reconstructions.

The Scattered Light Imaging Method (SLIM) was applied to measure the nonlinear refractive index of scattering media. The measurements are based on the analysis of the side-view images of the laser beam propagating inside highly scattering liquid suspensions. Proof-of-principle experiments were performed with colloids containing silica nanoparticles that behave as light scatterers. The technique allows measurements with lasers operating with arbitrary repetition rate as well as in the single-shot regime. The new method shows advantages and complementarity with respect to the Z-scan technique which is not appropriate to characterize scattering media.

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