Superresolution of text from nonideal video

Texts represent an important class of information in our daily lives. This paper studies the problem of super-resolution (SR) of texts, namely reconstructing high-resolution texts from low-resolution video captured by handheld cameras. Such type of video is called nonideal due to uncontrolled imaging condition, unknown point spread function and inevitable distortion caused by compression algorithms. Motivated by the different consideration in SR from mosaicing, we investigate the error accumulation in homography-based registration of multi-view images. We advocate the nonuniform interpolation approach towards SR that can achieve resolution scalability at a low computational cost and study the issues of phase consistency and uncertainty that are difficult to be addressed under the conventional framework of treating SR as an inverse problem. We also present a nonlinear diffusion aided blind deconvolution technique for simultaneous suppression of compression artifacts and enhancement of textual information. The performance of the proposed SR-of-texts technique is demonstrated by extensive experiments with challenging real-world sequences.