A Bayesian approach to image expansion for improved definition
IEEE Transactions on Image ProcessingPublished 1 May 1994
Richard R. Schultz, Robert Louis Stevenson
Citations534
SJR quartileQ1
SJR score2.50
SNIP3.41
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TL;DR
A method for nonlinear image expansion which preserves the discontinuities of the original image, producing an expanded image with improved definition is introduced.
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
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