Anisotropic Total Variation Method for Text Image Super-Resolution

This paper presents a text image super resolution algorithm based on total variation (TV). Text images typically consist of slim strokes on background. Thus, there are three different local characteristics as homogeneous, directed and complex on text image. Homogeneous region corresponds to background and directed means the region with dominant stroke direction and remaining is complex region. We proposed higher order smoothing on homogeneous region and anisotropic regularization on directed region which encodes the preference of edge direction by smoothing along preferred direction only. Required regularization terms are combined in proposed anisotropic TV functional and controlled by relating parameters. We calculated relating parameters byutilizing structure tensor field. Also to reduce the computational cost, we previously estimated nonchanging pixels and exclude them from calculation for speed up. Experiments shown that, proposed method performs better with low computational cost than general purpose TV on text image.