Restoration and recognition in a loop

Mithun Das Gupta, shyamsundar rajaram, nemanja petrovic, Thomas Huang

Abstract:

In this paper we present a novel learning based method for
restoring and recognizing images of digits that have been
blurred using an unknown kernel. The novelty of our work
is an iterative loop that alternates between recognition and
restoration stages. In the restoration stage we model the
image as an undirected graphical model over the image
patches with the compatibility functions represented as nonparametric kernel densities. Compatibility functions are
initially learned using uniform random samples from the
training data. We solve the inference problem by an extended version of the non-parametric belief propagation algorithm in which we introduce the notion of partial messages. We close the loop by using the confidence scores of
the recognition to non-uniformly sample from the training
set in order to retrain the compatibility functions. We show
experimental results on synthetic and license plate images.