  Most computer vision and machine learning-based approaches for historical
document analysis are tailored to grayscale or RGB images and thus, mostly
exploit their spatial information. Multispectral (MS) and hyperspectral (HS)
images contain, next to the spatial information, much richer spectral
information than RGB images (usually spreading beyond the visible spectral
range) that can facilitate more effective feature extraction, more accurate
classification and recognition, and thus, improved analysis. Although
utilization of rich spectral information can improve historical document
analysis tremendously, there are still some potential limitations of HS imagery
such as camera-induced noise and blur that require a carefully designed
preprocessing step. Here, we propose novel blind HS image deblurring methods
tailored to document images. We exploit a low-rank property of HS images (i.e.,
by projecting an HS image to a lower dimensional subspace) and utilize a text
tailor image prior to performing a PSF estimation and deblurring of subspace
components. The preliminary results show that the proposed approach gives good
results over all spectral bands, removing successfully image artefacts
introduced by blur and noise and significantly increasing the number of bands
that can be used in further analysis.
