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Proceedings Paper

Adaptive image interpolation using a multilayer neural network
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Paper Abstract

Image resizing is an important operation that is used extensively in document processing to magnify or reduce images. Standard approaches fit the original data with a continuous model and then resample this 2D function on a few sampling grid. These interpolation methods, however, apply an interpolation function indiscriminately to the whole image. The resulting document image suffers from objectionable moire patterns, edge blurring and aliasing. Therefore, image documents must often be segmented before other document processing techniques, such as filtering, resizing, and compression can be applied. In this paper, we present a new system to segment and label document images into text, halftone images, and background using feature extraction and unsupervised clustering. Once the segmentation is performed, a specific enhancement or interpolation kernel can be applied to each document component. In this paper, we demonstrate the power of our approach to segment document images into text, halftone, and background. The proposed filtering and interpolation method results in a noticeable improvement in the enhanced and resized image.

Paper Details

Date Published: 4 April 2001
PDF: 9 pages
Proc. SPIE 4305, Applications of Artificial Neural Networks in Image Processing VI, (4 April 2001); doi: 10.1117/12.420941
Show Author Affiliations
Mohamed Nooman Ahmed, Lexmark International, Inc. (United States)
Brian E. Cooper, Lexmark International, Inc. (United States)
Shaun T. Love, Lexmark International, Inc. (United States)

Published in SPIE Proceedings Vol. 4305:
Applications of Artificial Neural Networks in Image Processing VI
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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