Paper
18 December 2001 Enhancement of scanned documents in Besov spaces using wavelet domain representations
Author Affiliations +
Proceedings Volume 4670, Document Recognition and Retrieval IX; (2001) https://doi.org/10.1117/12.450723
Event: Electronic Imaging, 2002, San Jose, California, United States
Abstract
After scanning, a document is typically blurred, and some noise is introduced. Therefore, the enhancement process of a scanned document requires a denoising and deblurring step. Typically, these steps are performed using techniques originated in the Fourier-domain. It has been shown in many image processing applications such as compression and denoising that wavelet-domain processing outperforms Fourier-domain processing. One main reason for the success of wavelets is that wavelets adapt automatically to smooth and non-smooth parts in an image due to the link between wavelets and sophisticated smoothness spaces, the Besov spaces. Recently smoothing and sharpening of an image - interpreted as an increasing and decreasing of smoothness of an image - has been derived using Besov space properties. The goal of this paper is to use wavelet-based denoising and sharpening in Besov spaces in combination with characterization of lines and halftone patterns in the wavelet domain to build a complete wavelet-based enhancement system. It is shown that characteristics of a scanned document and the enhancement steps necessary for a digital copier application are well-suited to be modeled in terms of wavelet bases and Besov spaces. The modeling results leads to a very simple algorithmic implementation of a technique that qualitatively outperforms traditional Fourier-based techniques.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kathrin Berkner "Enhancement of scanned documents in Besov spaces using wavelet domain representations", Proc. SPIE 4670, Document Recognition and Retrieval IX, (18 December 2001); https://doi.org/10.1117/12.450723
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Cited by 3 scholarly publications.
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KEYWORDS
Wavelets

Halftones

Denoising

Scanners

Image processing

Image enhancement

Image segmentation

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