Single-frame text super-resolution: a bayesian approach

We address the problem of text super-resolution: given a single image of text scanned in at low resolution from a piece of paper, return the image that is mostly likely to be generated from a noiseless high-resolution scan of the same piece of paper. In doing so, we wish to: (1) avoid introducing artifacts in the high-resolution image such as blurry edges and rounded corners, (2) recover from quantization noise and grid-alignment effects that introduce errors in the low-resolution image, and (3) handle documents with very large glyph sets such as Japanese's Kanji. Applications for this technology include improving the display of: fax documents, low-resolution scans of archival documents, and low-resolution bitmapped fonts on high-resolution output devices.

• Type: Conference
• Year: 2004
• Where: ICIP
• Authors: Gerald Dalley, William T. Freeman, Joe Marks
• Keywords: Avoid Introducing Artifacts, ICIP 2004, Image Processing, Large Glyph Sets, Low-resolution Bitmapped Fonts, Low-resolution Image, Low-resolution Scans