Automated system for restoration of low-resolution document and text images

Abstract

Restoration of document and text images has become increasingly important in many areas of electronic imaging. This paper presents an automated system to restore low-resolution document and text images. It makes use of resolution expansion to enhance low-resolution images for optical character recognition accuracy as well as to improve the quality of degraded images. Several approaches have been proposed in the past for resolution expansion such as linear interpolation and cubic spline expansion. The proposed system implements a bimodal-smooth-average (BSA) scoring function as an optimal criterion for image quality. The BSA approach is very different from existing methods in the sense that it uses three measures: bimodal, smooth, and average to produce a scoring function as an optimal criterion. Its idea is to create for a given image a strongly bimodal image with smooth regions in both the foreground and background, while allowing for sharp discontinuities at the edges. Then the desired resolution-expanded image is obtained by solving a nonlinear optimization problem subject to a constraint that the average of expanded resolution must be equal to the original unexpanded resolution. The system can be used to restore both binary and grayscale images as well as video frames. Its capability is demonstrated experimentally to be quantitatively and qualitatively superior to standard interpolation methods.