An example-based prior model for text image super-resolution

This paper presents a prior model for text image super-resolution in the Bayesian framework. In contrast to generic image super-resolution task, super-resolution of text images can be benefited from strong prior knowledge of the image class: firstly, low-resolution images are assumed to be generated from a high-resolution image by a sort of degradation which can be grasped through example pairs of the original and the corresponding degradation; secondly, text images are composed of two homogeneous regions, text and background regions. These properties were represented in a Markov random field (MRF) framework. Experiments showed that our model is more appropriate to text image super-resolution than the other prior models.