Blind deconvolution: errors, errors everywhere

This paper focuses on a class of methods that accounts for uncertainty in the model as well as the data. Our example concerns spectroscopy - the attempt to reconstruct a true spectrum from an observed one. The problem we´re considering is sometimes called blind deconvolution, because we´re trying to unravel not only the spectrum, but the function that caused the blurring. These problems also arise in image deblurring.