A Sparse Coding Based Approach for the Resolution Enhancement and Restoration of Printed and Handwritten Textual Images

Sparse coding has shown to be an effective technique in solving various reconstruction tasks such as denoising, in painting, and resolution enhancement of natural images. In this paper, we explore the use of this technique specifically to deal with low-resolution and degraded textual images. Firstly, we propose a sparse coding based resolution enhancement approach to recover a textual image with higher resolution than the input low-resolution one. It is based on the use of multiple coupled dictionaries which are learned from a clustered training low-resolution/high-resolution patch-pair database. A reconstruction scheme is then suggested in order to adaptively select the appropriate dictionaries that are useful for better recovering each local patch. This approach can be applied for the magnification of both printed and handwritten characters. Secondly, we propose to integrate the magnification in a restoration framework specifically to denoise and reconstruct at the same time degraded characters. The performances of these propositions are evaluated on various types of degraded printed and handwritten textual images where loss of details and background noise exist. Promising results are achieved when compared with results of other existing approaches.