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Proceedings Paper

Algorithms for the resizing of binary and grayscale images using a logical transform
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Paper Abstract

The resizing of data, either upscaling or downscaling based on need for increased or decreased resolution, is an important signal processing technique due to the variety of data sources and formats used in today's world. Image interpolation, the 2D variation, is commonly achieved through one of three techniques: nearest neighbor, bilinear interpolation, or bicubic interpolation. Each method comes with advantages and disadvantages and selection of the appropriate one is dependent on output and situation specifications. Presented in this paper are algorithms for the resizing of images based on the analysis of the sum of primary implicants representation of image data, as generated by a logical transform. The most basic algorithm emulates the nearest neighbor technique, while subsequent variations build on this to provide more accuracy and output comparable to the other traditional methods. Computer simulations demonstrate the effectiveness of these algorithms on binary and grayscale images.

Paper Details

Date Published: 27 February 2007
PDF: 10 pages
Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 64970Z (27 February 2007); doi: 10.1117/12.704477
Show Author Affiliations
Ethan E. Danahy, Tufts Univ. (United States)
Sos S. Agaian, Univ. of Texas, San Antonio (United States)
Karen A. Panetta, Tufts Univ. (United States)

Published in SPIE Proceedings Vol. 6497:
Image Processing: Algorithms and Systems V
Jaakko T. Astola; Karen O. Egiazarian; Edward R. Dougherty, Editor(s)

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