A Bayesian approach to image expansion for improved definition

Author

Schultz, Richard R. ; Stevenson, Robert L.

Author_Institution

Dept. of Electr. Eng., Notre Dame Univ., IN, USA

Volume

3

Issue

3

fYear

1994

fDate

5/1/1994 12:00:00 AM

Firstpage

233

Lastpage

242

Abstract

Accurate image expansion is important in many areas of image analysis. Common methods of expansion, such as linear and spline techniques, tend to smooth the image data at edge regions. This paper introduces a method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition. The maximum a posteriori (MAP) estimation techniques that are proposed for noise-free and noisy images result in the optimization of convex functionals. The expanded images produced from these methods will be shown to be aesthetically and quantitatively superior to images expanded by the standard methods of replication, linear interpolation, and cubic B-spline expansion

Keywords

Bayes methods; estimation theory; image processing; Bayes method; convex functionals; discontinuities; edge regions; image analysis; image data; image definition; maximum a posteriori estimation; noise-free images; noisy images; nonlinear image expansion; optimization; Bayesian methods; Biomedical imaging; Face; Gaussian noise; Image edge detection; Image processing; Interpolation; Inverse problems; Satellites; Spline;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.287017

Filename

287017