  Using light spectra is an essential element in many applications, for
example, in material classification. Often this information is acquired by
using a hyperspectral camera. Unfortunately, these cameras have some major
disadvantages like not being able to record videos. Therefore, multispectral
cameras with wide-band filters are used, which are much cheaper and are often
able to capture videos. However, using multispectral cameras requires an
additional reconstruction step to yield spectral information. Usually, this
reconstruction step has to be done in the presence of imaging noise, which
degrades the reconstructed spectra severely. Typically, same or similar pixels
are found across the image with the advantage of having independent noise. In
contrast to state-of-the-art spectral reconstruction methods which only exploit
neighboring pixels by block-based processing, this paper introduces non-local
filtering in spectral reconstruction. First, a block-matching procedure finds
similar non-local multispectral blocks. Thereafter, the hyperspectral pixels
are reconstructed by filtering the matched multispectral pixels collaboratively
using a reconstruction Wiener filter. The proposed novel procedure even works
under very strong noise. The method is able to lower the spectral angle up to
18% and increase the peak signal-to-noise-ratio up to 1.1dB in noisy scenarios
compared to state-of-the-art methods. Moreover, the visual results are much
more appealing.
