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JACIII Vol.18 No.4 pp. 518-528
doi: 10.20965/jaciii.2014.p0518
(2014)

Paper:

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An Efficient Super Resolution Based on Image Dimensionality Reduction Using Accumulative Intensity Gradient

Muhammad Haris*, Kazuhito Sawase*,
Muhammad Rahmat Widyanto**, and Hajime Nobuhara*

*Department of Intelligent Interaction Technologies, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

**Computer Science Faculty, University of Indonesia, Depok, West Java, Indonesia

Received:
November 27, 2013
Accepted:
April 17, 2014
Published:
July 20, 2014
Creative Commons License
Keywords:
super-resolution, interpolation, gradient, edge-direction, remote-sensing
Abstract
An efficient super resolution algorithm based on edge direction is proposed based on the dimensional reduction of color images and 3 types of edge direction. The basic idea is to reduce image – especially color image – dimensions and to interpolate pixels by using 3 simple edge directions – vertical, horizontal, and diagonal. The proposed algorithm conceivably eliminate more color artifacts than Bicubic. The results of experiments using 30 natural images confirm that PSNR of the proposed method achieve the same quality as Fast Curvature Based Interpolation (FCBI). We confirmed that computation time for the proposed method was 40% shorter for RGB and 20% shorter for grayscale images than the previous fastest method, i.e., FCBI. We show efficient panorama image generation based on the proposed super resolution method as one application of our proposal.
Cite this article as:
M. Haris, K. Sawase, M. Widyanto, and H. Nobuhara, “An Efficient Super Resolution Based on Image Dimensionality Reduction Using Accumulative Intensity Gradient,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.4, pp. 518-528, 2014.
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References
  1. [1] W. Freeman, T. Jones, and E. Pasztor, “Example-based superresolution,” Computer Graphics and Applications, IEEE, Vol.22, No.2, pp. 56-65, Mar./Apr. 2002.
  2. [2] J. Qiao, J. Liu, and C. Zhao, “A Novel SVM-Based Blind Super-Resolution Algorithm,” In Int. Joint Conf. on Neural Networks 2006 (IJCNN ’06), pp. 2523-2528, 2006.
  3. [3] N. Asuni and A. Giachetti, “Accuracy Improvements and Artifacts Removal in Edge Based Image Interpolation,” VISAPP’08, No.1, pp. 58-65, 2008.
  4. [4] R. Hardie, “A Fast Image Super-Resolution Algorithm Using an Adaptive Wiener Filter,” IEEE Trans. on Image Processing, Vol.16, pp. 2953-2964, 2007.
  5. [5] G. Freedman and R. Fattal, “Image and Video Upscaling from Local Self-Examples,” ACM Trans. Graph., Vol.28, No.3, pp. 1-10, 2010.
  6. [6] Y. Shuyuan, L. Zhizhou,W. Min, S. Fenghua, and J. Licheng, “Multitask dictionary learning and sparse representation based singleimage super-resolution reconstruction,” Neurocomputing, Vol.74, No.17, pp. 3193-3203, 2011.
  7. [7] B. Ahrens, “Genetic Algorithm Optimization on Super Resolution Parameters,” Genetic and Evolutionary Computation, Vol.1, pp. 58-65, 2005.
  8. [8] D. Glasner, S. Bagon, and M. Irani, “Super-Resolution from a Single Image,” ICCV’09, 2009.
  9. [9] Q. Shan, Z. Li, J. Jia, and C.-K. Tang, “Fast Image/Video Upsampling,” ACM Trans. on Graphics (SIGGRAPH ASIA), 2008.
  10. [10] C.-Y. Yang, J.-B. Huang, and M.-H. Yang, “Exploiting selfsimilarities for single frame super-resolution,” Proc. of the 10th Asian Conf. on Computer Vision, pp. 497-510, 2011.
  11. [11] L. C. Pickup, S. J. Roberts, and A. Zisserman, “Optimizing and Learning for Super-resolution,” Proc. of the British Machine Vision Conf., 2006.
  12. [12] M. Nuno-Maganda and M. Arias-Estrada, “Real-time FPGA-based architecture for bicubic interpolation: an application for digital image scaling,” Int. Conf. on Reconfigurable Computing and FPGAs 2005 (ReConFig 2005), 2005.
  13. [13] X. Li and M. T. Orchard, “New Edge-Directed Interpolation,” IEEE Trans. on Image Processing, Vol.10, pp. 1521-1527, 2001.
  14. [14] M.-J. Chen, C.-H. Huang., and W.-L. Lee, “A fast edge-oriented algorithm for image interpolation,” Image and Vision Computing, Vol.23, No.9, pp. 791-798, 2005.
  15. [15] K. Hirakawa and T. Parks, “Adaptive homogeneity-directed demosaicing algorithm,” IEEE Trans. on Image Processing, Vol.14, No.3, pp. 360-369, 2005.
  16. [16] A. Giachetti and N. Asuni, “Real time artifact-free image upscaling,” IEEE Trans. on Image Processing, Vol.20, No.10, pp. 2760-2768, October 2011.
  17. [17] M. Haris, K. Sawase, T. Sawada, K. Kamijima, M. R. Widyanto, and H. Nobuhara, “Parameter Optimization Of Fast Curvature Based Interpolation Using Genetic Algorithm,” ISCIIA’12, 2012.
  18. [18] Z.Wang and A. Bovik, “Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures,” Signal ProcessingMagazine, IEEE, Vol.26, No.1, pp. 98-117, 2009.
  19. [19] R. Ehsani, S. Sankaran, J. Maja, and F. Garcia, “Advanced Tree-Stress-Detection Technologies For Citrus,” Citrus Industry, pp. 6-7, May 2012.

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