  Light spectra are a very important source of information for diverse
classification problems, e.g., for discrimination of materials. To lower the
cost for acquiring this information, multispectral cameras are used. Several
techniques exist for estimating light spectra out of multispectral images by
exploiting properties about the spectrum. Unfortunately, especially when
capturing multispectral videos, the images are heavily affected by noise due to
the nature of limited exposure times in videos. Therefore, models that
explicitly try to lower the influence of noise on the reconstructed spectrum
are highly desirable. Hence, a novel reconstruction algorithm is presented.
This novel estimation method is based on the guided filtering technique which
preserves basic structures, while using spatial information to reduce the
influence of noise. The evaluation based on spectra of natural images reveals
that this new technique yields better quantitative and subjective results in
noisy scenarios than other state-of-the-art spatial reconstruction methods.
Specifically, the proposed algorithm lowers the mean squared error and the
spectral angle up to 46% and 35% in noisy scenarios, respectively. Furthermore,
it is shown that the proposed reconstruction technique works out-of-the-box and
does not need any calibration or training by reconstructing spectra from a
real-world multispectral camera with nine channels.
