Image Interpolation by Pixel-Level Data-Dependent Triangulation
We present a novel image interpolation algorithm. The algorithm can be used in arbitrary resolution enhancement, arbitrary rotation and other applications of still images in continuous space. High‐resolution images are interpolated from the pixel‐level data‐dependent triangulation of lower‐resolutio...
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| Vydáno v: | Computer graphics forum Ročník 23; číslo 2; s. 189 - 201 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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9600 Garsington Road , Oxford , OX4 2DQ , UK
Blackwell Publishing Ltd
01.06.2004
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | We present a novel image interpolation algorithm. The algorithm can be used in arbitrary resolution enhancement, arbitrary rotation and other applications of still images in continuous space. High‐resolution images are interpolated from the pixel‐level data‐dependent triangulation of lower‐resolution images. It is simpler than other methods and is adaptable to a variety of image manipulations. Experimental results show that the new “mesh image” algorithm is as fast as the bilinear interpolation method. We assess the interpolated images' quality visually and also by the MSE measure which shows our method generates results comparable in quality to slower established methods. We also implement our method in graphics card hardware using OpenGL which leads to real‐time high‐quality image reconstruction. These features give it the potential to be used in gaming and image‐processing applications. |
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| AbstractList | We present a novel image interpolation algorithm. The algorithm can be used in arbitrary resolution enhancement, arbitrary rotation and other applications of still images in continuous space. High‐resolution images are interpolated from the pixel‐level data‐dependent triangulation of lower‐resolution images. It is simpler than other methods and is adaptable to a variety of image manipulations. Experimental results show that the new “mesh image” algorithm is as fast as the bilinear interpolation method. We assess the interpolated images' quality visually and also by the MSE measure which shows our method generates results comparable in quality to slower established methods. We also implement our method in graphics card hardware using OpenGL which leads to real‐time high‐quality image reconstruction. These features give it the potential to be used in gaming and image‐processing applications. We present a novel image interpolation algorithm. The algorithm can be used in arbitrary resolution enhancement, arbitrary rotation and other applications of still images in continuous space. High-resolution images are interpolated from the pixel-level data-dependent triangulation of lower-resolution images. It is simpler than other methods and is adaptable to a variety of image manipulations. Experimental results show that the new "mesh image" algorithm is as fast as the bilinear interpolation method. We assess the interpolated images' quality visually and also by the MSE measure which shows our method generates results comparable in quality to slower established methods. We also implement our method in graphics card hardware using OpenGL which leads to real-time high-quality image reconstruction. These features give it the potential to be used in gaming and image-processing applications. [PUBLICATION ABSTRACT] |
| Author | Su, Dan Willis, Philip |
| Author_xml | – sequence: 1 givenname: Dan surname: Su fullname: Su, Dan organization: Department of Computer Science, University of Bath, U.K. mapds, P.J.Willis@bath.ac.uk – sequence: 2 givenname: Philip surname: Willis fullname: Willis, Philip organization: Department of Computer Science, University of Bath, U.K. mapds, P.J.Willis@bath.ac.uk |
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| Cites_doi | 10.1109/83.841940 10.1109/ICASSP.1995.479972 10.1016/S0262-8856(02)00089-6 10.1109/ICASSP.1992.226246 10.1002/scj.4690250609 10.1109/ICIP.1998.999013 10.1016/0165-1684(91)90114-X 10.1109/ICIP.1998.727167 10.1109/83.287017 10.1109/ICIP.2000.899369 10.1016/0167-8396(90)90037-R 10.1109/TPCG.2003.1206926 10.1109/ICIP.1996.560768 10.1126/science.1734518 10.1109/TASSP.1981.1163711 10.1109/34.75515 10.1007/978-1-4899-6950-7 10.1093/imanum/10.1.137 10.1109/ICIP.1995.537460 10.1109/83.366477 |
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| References_xml | – reference: A. N. Netravali and B. G. Hasskell. Digital Pictures: Representation, Compression and Standards, 2nd edition, Plenum Press, New York , 1995. – reference: R. R. Schultz and R. L. Stevenson. A Bayesian Approach to Image Expansion for Improved Definition. IEEE Transactions on Image Processing, 3(3):233-242, 1994. – reference: K. Jensen and D. Anastassiou. Subpixel Edge Localization and the Interpolation of Still Images. IEEE Transactions on Image Processing, vol. 4, No. 3, pp. 285-295, 1995. – reference: D. C. Van Essen, C. H. Anderson and D. J. Felleman. Information Processing in the Primate Visual System: An Integrated System Perspective, Science, 255(5043):419-423, 1992. – reference: E. Quak and L. L. Schumaker. Cubic spline interpolation using data dependent triangulations. Computer Aided Geometry Design, 7: 293-301, 1990. – reference: R. Keys. Cubic Convolution Interpolation for Digital Image Processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(6):1153-1160, 1981. – reference: S. Battiato, G. Gallo and F. Stanco. A locally-adaptive zooming algorithm for digital images. Image and Vision Computing, 20(11):805-812, September 2002. – reference: N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans and A. C. Bovik. Image Quality assessment based on a degradation model. IEEE Transactions on Image Processing, 9: 636-650, 2000. – reference: N. B. Karayiannis and A. N. Venetsanopoulos. Image Interpolation Based on Variational Principles. Signal Process, 25: 259-288, 1991. – reference: X. Yu, B. Morse and T. W. Sederberg. Image Reconstruction Using Data-Dependent Triangulation. IEEE Computer Graphics and Applications, 21(3):62-68, MayJune 2001. – reference: N. Dyn, D. Levin and S. Rippa. Data Dependent Triangulations for Piecewise Linear Interpolation. IMA Journal of Numerical Analysis, 10: 127-154, Institute of Mathematics and its Applications , 1990. – reference: M. Unser, A. Aldroubi and M. Eden. Fast B-Spline Transforms for Continuous Image Representation and Interpolation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(3):277-285, 1991. – reference: E. Shinbori and M. Takagi. High Quality Image Magnification Applying the Gerchberg-Papoulis Iterative Algorithm with DCT. 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| SubjectTerms | Algorithms C.5.4 [Computer System Implementation]: VLSI systems Computer graphics continuous images display algorithms graphics data structures and data types I.3.3 [Computer Graphics]: Picture/image generation - digitizing and scanning I.3.3 [Computer Graphics]: Picture/image generation – digitizing and scanning, display algorithms, viewing algorithms I.3.6 [Computer Graphics]: Methodology and techniques - device independence I.3.6 [Computer Graphics]: Methodology and techniques – device independence, graphics data structures and data types I.4.1 [Image Processing and Computer Vision]: Digitisation and image capture - image geometry image magnification image modelling Image processing systems image representation picture interpolation picture manipulation Studies viewing algorithms |
| Title | Image Interpolation by Pixel-Level Data-Dependent Triangulation |
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