Research Projects

Image Magnification

This line of work seeks to improve techniques for image magnification using level-set, Bayesian, sparse data interpolation and other methods to improve contour smoothness and reduce jaggies.

Funding

This work has been funded in part Adobe Systems.

Students

• Neil Toronto
• Jacob Merrell
• Dan Goggins
• Duane Schwartzwald
• Xiaohua Yu (with Dr. Thomas Sederberg)

Publications and Theses

N. Toronto, B. Morse, D. Ventura, and K. Seppi. Super-resolution via recapture and Bayesian effect modeling. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2009.

J. Merrell. Generalized constrained interpolation. Master's thesis, Brigham Young University, 2008.

N. Toronto, D. Ventura, and B. Morse. Edge inference for image interpolation. In Proceedings IEEE International Joint Conference on Neural Networks, pages 1782-1787, August 2005.

D. Goggins. Constraint-based interpolation. Master's thesis, Brigham Young University, 2005.

B. S. Morse and D. Schwartzwald. Image magnification using level-set image reconstruction. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 333-340. IEEE Computer Society Press, December 2001.

X. Yu, B. S. Morse, and T. W. Sederberg. Image reconstruction using data-dependent triangulation. IEEE Computer Graphics & Applications, 21(3):62–68, March 2001.

B. S. Morse and D. Schwartzwald. Isophote-based interpolation. In IEEE International Conference on Image Processing (ICIP), pages 227-231, October 1998.

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