We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Apparent Resolution Enhancement: Structural Similarity Perspective

by Rehanullah Khan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 134 - Number 3
Year of Publication: 2016
Authors: Rehanullah Khan
10.5120/ijca2016907860

Rehanullah Khan . Apparent Resolution Enhancement: Structural Similarity Perspective. International Journal of Computer Applications. 134, 3 ( January 2016), 18-22. DOI=10.5120/ijca2016907860

@article{ 10.5120/ijca2016907860,
author = { Rehanullah Khan },
title = { Apparent Resolution Enhancement: Structural Similarity Perspective },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 134 },
number = { 3 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume134/number3/23894-2016907860/ },
doi = { 10.5120/ijca2016907860 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:33:09.683707+05:30
%A Rehanullah Khan
%T Apparent Resolution Enhancement: Structural Similarity Perspective
%J International Journal of Computer Applications
%@ 0975-8887
%V 134
%N 3
%P 18-22
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a detailed investigation and comparative analysis of the two well-known classes of Super Resolution (SR) i.e. Reconstruction Based Super Resolution (RBSR) and the Example Based Super Resolution (EBSR), taking into account the seven variables: Resolution Factor, Aliasing, Stills, Motion, Compression, Noise and Detection scenario. The EBSR uses high and low frequency relationship and the RBSR is based on the frames sequence information. The EBSR and RBSR are tested on number of images to investigate which of the two classes of SR algorithms are best suited for preserving structural similarity to the original image and for visual analysis of the SR image. Experimental results show that over-all SSIM index for the EBSR is higher than RBSR and thus preserves the image quality better compared to the RBSR. In an evaluation of EBSR and RBSR for feature based detection scenario, it is observed that face detection in the EBSR resultant image has the same performance compared to that of the Viola-Jones approach [21] without SR; therefore, we do not gain any improvement in EBSR. In the case of RBSR, due to the registration errors, the over-all face detection performance after SR by the Viol-Jones algorithm is reduced by 3%. As such, it is an extension of the author’s conference work [12].

References
  1. S. Baker and T. Kanade 2002, “Limits on super-resolution and how to break them,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1167 – 1183.
  2. J. R. Bergen, P. Anandan, K. J. Hanna, and R. Hingorani 1992, “Hierarchical model-based motion estimation,” in ECCV ’92: Proceedings of the Second European Conference on Computer Vision. London, UK: Springer-Verlag, pp. 237–252.
  3. D. Capel and A. Zisserman 2000, “Super-resolution enhancement of text image sequences,” in in Proc. International Conference on Pattern Recognition. IEEE Computer Society Press, pp. 600–605.
  4. M. chao Chiang and T. E. Boult 1996, “Efficient image warping and super resolution,” in In IEEE Workshop on Applications of Computer Vision (WACV96. IEEE Computer Society, pp. 56–61. M.
  5. Elad and A. Feuer 1997, “Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images,” Image Processing, IEEE Transactions on, vol. 6, no. 12, pp. 1646–1658.
  6. P. E. Eren, M. I. Sezan, and A. M. Tekalp 1997, “Robust, object-based high-resolution image reconstruction from low-resolution video,” IEEE Transactions on Image Processing, vol. 6, no. 10, pp. 1446–1451.
  7. W. T. Freeman, T. R. Jones, and E. C. Pasztor 2002, “Example-based superresolution,” IEEE Computer Graphics and Applications, vol. 22, no. 2, pp. 56–65.
  8. R. Hardie, K. Barnard, and E. Armstrong 1997, “Joint map registration and high resolution image estimation using a sequence of undersampled images,” vol. 6, no. 12, pp. 1621–1633.
  9. M. Irani and S. Peleg 1991, “Improving resolution by image registration,” CVGIP: Graph. Models Image Process., vol. 53, no. 3, pp. 231–239.
  10. Z. Jiang, T.-T. Wong, and H. Bao 2003, “Practical super-resolution from dynamic video sequences,” in CVPR , pp. 549–554.
  11. D. Keren, S. Peleg, and R. Brada 1988, “Image sequence enhancement using sub-pixel displacements,” pp. 742–746.
  12. R. Khan, R. Sablatnig, A. Bais, and Y. M. Khawaja 2011, “Comparison of reconstruction and example-based super-resolution,” in IEEE ICET, pp. 1–6.
  13. F. Lin, S. Denman, V. Chandran, and S. Sridharan 2007, “Automatic tracking, super-resolution and recognition of human faces from surveillance video,” in MVA, pp. 37–40.
  14. F. C. Lin, S. Denman, V. Chandran, and S. Sridharan 2007, “Automatic tracking, super-resolution and recognition of human faces from surveillance video,” in IAPR Conference on Machine Vision Applications. The International Association for Pattern Recognition, pp. 37–40.
  15. F. Liu, J. Wang, S. Zhu, M. Gleicher, and Y. Gong 2008, “Noisy video superresolution,” in MM ’08: Proceeding of the 16th ACM international conference on Multimedia. New York, NY, USA: ACM, pp. 713–716.
  16. A. J. Patti, M. I. Sezan, and A. M. Tekalp 1997, “Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time,” IEEE Transactions on Image Processing, vol. 6, pp. 1064–1076.
  17. T. Q. Pham, L. J. van Vliet, and K. Schutte 2006, “Resolution enhancement of low-quality videos using a high-resolution frame,” J. G. Apostolopoulos and A. Said, Eds., vol. 6077, no. 1. SPIE, pp. 607–708.
  18. R. R. Schultz and R. L. Stevenson 1996, “Extraction of high-resolution frames from video sequences,” IEEE Transactions on Image Processing, vol. 5, pp. 996–1011,.
  19. E. Shechtman, Y. Caspi, and M. Irani 2002, “Increasing space-time resolution in video,” in ECCV ’02: Proceedings of the 7th European Conference on Computer Vision-Part I. London, UK: Springer-Verlag, pp. 753–768.
  20. R. Y. Tsai and T. S. Huang 1984, “Multi-frame image restoration and registration,” in Advances in Computer Vision and Image Processing, Greenwich, CT, pp. 317–339.
  21. P. Viola and M. J. Jones 2004, “Robust real-time face detection,” IJCV, vol. 57, no. 2, pp. 137–154.
  22. Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli 2004, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, pp. 600–612.
  23. G. Wolberg 1990, Digital Image Warping. IEEE Computer Society Press, Los Alamitos, CA.
  24. A. Zomet, A. Rav-acha, and S. Peleg 2001, “Robust super resolution,” In Proc. of the IEEE Workshop on Applications of Computer Vision, pp. 645–650.
  25. H. Chang, D. Y. Yeung, and Y. Xiong 2004, Super-resolution through neighbor embedding. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 275–282.
Index Terms

Computer Science
Information Sciences

Keywords

EBSR RBSR SR RSR SSIM Index

Powered by PhDFocusTM