Super-Resolution of Text Images Using Edge-Directed Tangent Field

This paper presents an edge-directed super-resolution algorithm for gray level document images without using any training set. This technique creates an image with smooth regions in both the foreground and the background, while allowing sharp discontinuities across and smoothness along the edges. Our method preserves sharp corners in text images by using the local edge direction, which is computed first by evaluating the gradient field and then taking its tangent. Super-resolution of document images is characterized by bimodality, smoothness along the edges as well as subsampling consistency. These characteristics are enforced in a Markov Random Field (MRF) framework by defining an appropriate energy function. In our case, subsampling of super-resolution image will return the original low-resolution one, proving the correctness of the method. The super-resolution image, is generated by iteratively reducing this energy function. Experimental results on a wide variety of input images, d...

• Type: Conference
• Year: 2008
• Where: DAS
• Authors: Jyotirmoy Banerjee, C. V. Jawahar
• Keywords: DAS 2008, Document Analysis, Document Image, Energy Function, Level Document Images