The Community for Technology Leaders
RSS Icon
Issue No.02 - March/April (2002 vol.22)
pp: 56-65
<p>Image-based models for computer graphics lack resolution independence: they cannot be enlarged much beyond the pixel resolution they were sampled at without a degradation of quality. Interpolating images usually results in a blurring of edges and image details. We describe image interpolation algorithms which use a database of training images to create plausible high-frequency details in zoomed images. Image preprocessing steps allow the use of image detail from regions of the training images which may look quite different than the image to be processed. These methods preserve fine details, such as edges, generate believable textures, and can give good results even after enlarging multiple octaves.</p>
William T. Freeman, Thouis R. Jones, Egon C Pasztor, "Example-Based Super-Resolution", IEEE Computer Graphics and Applications, vol.22, no. 2, pp. 56-65, March/April 2002, doi:10.1109/38.988747
1. W.T. Freeman, E.C. Pasztor, and O.T. Carmichael, “Learning Low-Level Vision,” Int'l J. Computer Vision, vol. 20, no. 1, pp. 25-47, 2000.
2. W.T. Freeman and E.C. Pasztor, "Learning to Estimate Scenes from Images," Adv. Neural Information Processing Systems, M.S. Kearns, S.A. Solla, and D.A. Cohn, eds., vol. 11,MIT Press, Cambridge, Mass., 1999, pp. 775-781.
3. W.T. Freeman and E.C. Pasztor, "Markov Networks for Superresolution," Proc. 34th Ann. Conf. Information Sciences and Systems (CISS 2000), Dept. Electrical Eng., Princeton Univ., 2000.
4. A. Hertzmann et al., "Image Analogies," Computer Graphics (Proc. Siggraph 2001), ACM Press, New York,2001, pp. 327-340.
5. S. Baker and T. Kanade, “Limits on Super-Resolution and How to Break Them,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
6. C. Liu, H. Shum, and C. Zhang, "A Two-Step Approach to Hallucinating Faces: Global Parametric Model and Local Non-Parametric Model," Proc. Int'l Conf. Computer Vision (ICCV), vol. I,IEEE CS Press, Los Alamitos, Calif., 2001, pp. 192-198.
7. D.J. Field, "What Is the Goal of Sensory Coding," Neural Computation, vol. 6, no. 4, July 1994, pp. 559-601.
8. O. Schwartz and E.P. Simoncelli, "Natural Signal Statistics and Sensory Gain Control," Nature Neuroscience, vol. 4, no. 8, Aug. 2001, pp. 819-825.
9. S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distribution, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 4, Nov. 1984, pp. 721-741.
10. J. Pearl, Probabilistic Reasoning in Intelligent Systems. San Mateo, Calif.: Morgan Kaufman, 1988.
11. J.S. Yedidia, W.T. Freeman, and Y. Weiss, "Generalized Belief Propagation," Advances in Neural Information Processing Systems, T.K. Leen, T.G. Dietterich, and V. Tresp, eds., vol. 13,MIT Press, Cambridge, Mass., 2001, pp. 689-695.
12. S.A. Nene and S.K. Nayar, "A Simple Algorithm for Nearest Neighbor Search in High Dimensions," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 9, Sept. 1997, pp. 989-1003.
13. A.A. Efros and W.T. Freeman, "Image Quilting for Texture Synthesis and Transfer," Computer Graphics (Proc. Siggraph 2001), ACM Press, New York, 2001, pp. 341-346.
14. L. Liang et al. "Real-Time Texture Synthesis by Patch-Based Sampling," ACM Trans. Graphics, vol. 20, no. 3, July 2001, pp. 127-150.
15. A. Pentland and B. Horowitz, "A Practical Approach to Fractal-Based Image Compression," Digital Images and Human Vision, A.B. Watson, ed., MIT Press, Cambridge, Mass., 1993.
16. W.F. Schreiber, Fundamentals of Electronic Imaging Systems, Springer-Verlag, New York, 1986.
38 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool