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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 953476, 8 pages
http://dx.doi.org/10.1155/2014/953476
Research Article

A Block-Based Regularized Approach for Image Interpolation

1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China
2Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan 430081, China

Received 1 November 2013; Accepted 27 December 2013; Published 11 February 2014

Academic Editor: Yi-Hung Liu

Copyright © 2014 Li Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Zhao, H. Feng, Z. Xu, and Q. Li, “An improved image deconvolution approach using local constraint,” Optics and Laser Technology, vol. 44, no. 2, pp. 421–427, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Tian and K.-K. Ma, “A survey on super-resolution imaging,” Signal, Image and Video Processing, vol. 5, no. 3, pp. 329–342, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Shanmugavadivu and K. Balasubramanian, “Particle swarm optimized multi-objective histogram equalization for image enhancement,” Optics and Laser Technology, vol. 57, pp. 243–251, 2014. View at Publisher · View at Google Scholar
  4. H. Yang, Z. Zhang, M. Zhu, and H. Huang, “Edge-preserving image deconvolution with nonlocal domain transform,” Optics and Laser Technology, vol. 54, pp. 128–136, 2013. View at Google Scholar
  5. X. Li and M. T. Orchard, “New edge-directed interpolation,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1521–1527, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. J. W. Hwang and H. S. Lee, “Adaptive image interpolation based on local gradient features,” IEEE Signal Processing Letters, vol. 11, no. 3, pp. 359–362, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. D. D. Muresan and T. W. Parks, “Adaptively quadratic (AQua) image interpolation,” IEEE Transactions on Image Processing, vol. 13, no. 5, pp. 690–698, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Wang, S. Xiang, G. Meng, H. Wu, and C. Pan, “Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 8, pp. 1289–1299, 2013. View at Google Scholar
  9. H. Xu, G. Zhai, and X. Yang, “Single image super-resolution with detail enhancement based on local fractal analysis of gradient,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 10, pp. 1740–1754, 2013. View at Google Scholar
  10. F. Zhou, W. Yang, and Q. Liao, “Interpolation-based image super-resolution using multisurface fitting,” IEEE Transactions on Image Processing, vol. 21, no. 7, pp. 3312–3318, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  11. V. Magudeeswaran and C. G. Ravichandran, “Fuzzy logic-based histogram equalization for image contrast enhancement,” Mathematical Problems in Engineering, vol. 2013, Article ID 891864, 10 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  12. T. Liu and Z. Xiang, “Image restoration combining the second-order and fourth-order PDEs,” Mathematical Problems in Engineering, vol. 2013, Article ID 743891, 7 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  13. X. Liu, D. Zhao, R. Xiong, S. Ma, W. Gao, and H. Sun, “Image interpolation via regularized local linear regression,” IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3455–3469, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  14. A. N. Tikhonov, Equations of Mathematical Physics, vol. 39, Dover Publications, New York, NY, USA, 1963.
  15. R. L. Lagendijk and K. Biemond, Iterative Identification and Restoration of Images, Kluwer Academic Publishers, Boston, Mass, USA, 1990.
  16. N. P. Galatsanos and A. K. Katsaggelos, “Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation,” IEEE Transactions on Image Processing, vol. 1, no. 3, pp. 322–336, 1992. View at Publisher · View at Google Scholar · View at Scopus