An assisted multi-frame approach for super-resolution in hyperspectral images of rock samples

Imaging spectroscopy is decisive for accurately characterizing rock samples for geological and mineral applications, since slight differences in mineral composition can be easily recognized in spectral signatures. Although, instrument limitations prevent high spatial resolution pixels scanned along hundreds of spectral channels, which considerably reduces the data capabilities. Multi frame Super-Resolution (SR) techniques can overcome this issue by retrieving high-quality images using purely computational methods. However, these approaches still have important drawbacks because of inherent uncertainties when estimating the spatial variation between frames, which strongly penalize the spectral information, essential for lithology. In this paper we propose an assisted framework to provide fully controlled motion parameters of the imaging process, skipping the intricate registration task. Essentially, we use a stepping device to successively change the position of rock samples for each new frame acquisition. With accurate knowledge about motion parameters, we were able to overcome the uncertainties regarding spatial registration between frames. Therefore, a reasonable number of equations connecting the low-resolution frames and the super resolved image could be used to solve the optimization problem. Extensive experiments proved that the proposed assisted method achieved best performances (both qualitatively and quantitatively) compared with similar approaches. Moreover, detailed spectral analysis showed almost absolute consistency between the original low-resolution image and the super-resolved result, which is crucial to guarantee reliable mineral characterization.