image magnification, interpolation, smooth reconstructions
Image magnification is a common problem in imaging applications, requiring interpolation to “read between the pixels”. Although many magnification/interpolation algorithms have been proposed in the literature, all methods must suffer to some degree the effects of impefect reconstruction―false high-frequency content introduced by the underlying original sampling. Most often, these effects manifest themselves as jagged contours in the image. This paper presents a method for constrained smoothing of such artifacts that attempts to produce smooth reconstructions of the image’s level curves while still maintaining image fidelity. This is similar to other iterative reconstruction algorithms and to Bayesian restoration techniques, but instead of assuming a smoothness prior for the underlying intensity function it assumes smoothness of the level curves. Results show that this technique can produce images whose error properties are equivalent to the initial approximation (interpolation) used while their contour smoothness is both visually and quantitatively improved.
Original Publication Citation
B. S. Morse and D. Schwartwald, "Image magnification using level-set image reconstruction," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 333-34, IEEE Computer Society Press, December 21.
BYU ScholarsArchive Citation
Morse, Bryan S. and Schwartzwald, Duane, "Image Magnification Using Level-Set Reconstruction" (2001). All Faculty Publications. 554.
Physical and Mathematical Sciences
© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information