Paper
21 May 2004 Digital image interpolation using adaptive Gaussian basis functions
Author Affiliations +
Proceedings Volume 5299, Computational Imaging II; (2004) https://doi.org/10.1117/12.555598
Event: Electronic Imaging 2004, 2004, San Jose, California, United States
Abstract
Digital image interpolation using Gaussian radial basis functions has been implemented by several investigators, and promising results have been obtained; however, determining the basis function variance has been problematic. Here, adaptive Gaussian basis functions fit the mean vector and covariance matrix of a non-radial Gaussian function to each pixel and its neighbors, which enables edges and other image characteristics to be more effectively represented. The interpolation is constrained to reproduce the original image mean gray level, and the mean basis function variance is determined using the expected image smoothness for the increased resolution. Test outputs from the resulting Adaptive Gaussian Interpolation algorithm are presented and compared with classical interpolation techniques.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Terence D. Hunt and Steven C. Gustafson "Digital image interpolation using adaptive Gaussian basis functions", Proc. SPIE 5299, Computational Imaging II, (21 May 2004); https://doi.org/10.1117/12.555598
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KEYWORDS
Digital imaging

Image resolution

Image interpolation

Image analysis

Image enhancement

Linear filtering

Super resolution

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