Browse > Article
http://dx.doi.org/10.9708/jksci.2011.16.6.071

Local Block Learning based Super resolution for license plate  

Shin, Hyun-Hak (Dept. of Visual Information Processing, Korea University)
Chung, Dae-Sung (Dept. of Visual Information Processing, Korea University)
Ku, Bon-Hwa (Dept. of Electrical Engineering, Korea University)
Ko, Han-Seok (Dept. of Electrical Engineering, Korea University)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.6, 2011 , pp. 71-77 More about this Journal
Abstract
In this paper, we propose a learning based super resolution algorithm using local block for image enhancement of vehicle license plate. Local block is defined as the minimum measure of block size containing the associative information in the image. Proposed method essentially generates appropriate local block sets suitable for various imaging conditions. In particular, local block training set is first constructed as ordered pair between high resolution local block and low resolution local block. We then generate low resolution local block training set of various size and blur conditions for matching to all possible blur condition of vehicle license plates. Finally, we perform association and merging of information to reconstruct into enhanced form of image from training local block sets. Representative experiments demonstrate the effectiveness of the proposed algorithm.
Keywords
Learning based super resolution; License plate enhancement; Local block;
Citations & Related Records
연도 인용수 순위
  • Reference
1 E. B. Sudderth, A. T. Iher, W. T. Freeman, and A. S. Willsky, "Nonparametric belief propagation," in proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03), vol. 1, pp. 605-612, June, 2003.
2 S. Rajaram, M. D. Gupta, N. Petrovic, and T. S. Huang, "Non-parametric image super resolution using multiple images," in Proceedings of IEEE International Conference on Image Processing (ICIP'05), September, 2005.
3 S. Rajaram, M. D. Gupta, N. Petrovic, and T. S. Huang, "Learning-based nonparametric image super-resolution," EURASIPJ. Appl. Signal Process, pp.229-240, 2006.
4 S. Baker, T. Kanade, "Limits on super-resolution and how to break them," in proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 372-379, 2000.
5 M. Isard, "PAMPAS: real-valued graphical models for computer vision," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol.1, pp.613-620, June, 2003.
6 S. Farisu, M. D. Robinson, and M. Elad, "Fast and robust multi-frame super resolution," IEEE Transaction on Image Processing, 2004.
7 S. Chaudhuri, M. V. Joshi, "Motion-free superresolution," Springer, New York, USA.
8 D. Glasner, S. Bagon, and M. Irani, "Super-resolution from a single image," International Conference on Computer Vision (ICCV), October, 2009.