  Blind image deblurring (BID) is an ill-posed inverse problem, usually
addressed by imposing prior knowledge on the (unknown) image and on the
blurring filter. Most of the work on BID has focused on natural images, using
image priors based on statistical properties of generic natural images.
However, in many applications, it is known that the image being recovered
belongs to some specific class (e.g., text, face, fingerprints), and exploiting
this knowledge allows obtaining more accurate priors. In this work, we propose
a method where a Gaussian mixture model (GMM) is used to learn a class-adapted
prior, by training on a dataset of clean images of that class. Experiments show
the competitiveness of the proposed method in terms of restoration quality when
dealing with images containing text, faces, or fingerprints. Additionally,
experiments show that the proposed method is able to handle text images at high
noise levels, outperforming state-of-the-art methods specifically designed for
BID of text images.
