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Single and Multi frame Image super-resolution and its Performance Analysis: A Comprehensive Survey

by P B Chopade, P M Patil
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 15
Year of Publication: 2015
Authors: P B Chopade, P M Patil
10.5120/19616-1510

P B Chopade, P M Patil . Single and Multi frame Image super-resolution and its Performance Analysis: A Comprehensive Survey. International Journal of Computer Applications. 111, 15 ( February 2015), 29-34. DOI=10.5120/19616-1510

@article{ 10.5120/19616-1510,
author = { P B Chopade, P M Patil },
title = { Single and Multi frame Image super-resolution and its Performance Analysis: A Comprehensive Survey },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 15 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number15/19616-1510/ },
doi = { 10.5120/19616-1510 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:59.909106+05:30
%A P B Chopade
%A P M Patil
%T Single and Multi frame Image super-resolution and its Performance Analysis: A Comprehensive Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 15
%P 29-34
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Super-resolution is the process of combining one or more low –resolution images to obtain a high –resolution image. It has been a very interesting topic for the research over the last few years. It is used for practical applications in more realistic problems faced in different areas, which range over satellite and aerial imaging of biomedical image processing as well as in daily routine life like different biometrics in offices and industries. In this survey article, more focus is given on basic algorithms and their classification based methodology used to implement it. Due to its vast scope of applications researchers are developing a novel suerresolution algorithm for a specific intention based on single and multiframe image resolution. The proposed comprehensive survey gives an overview of most of published works based on its performance analysis. In this survey, the basic concepts of the algorithms are explained and then their performance analyses through which each of these methods have developed are mentioned in detail. Furthermore, different issues in superresolution algorithms for single and multiframesuch as models and registration algorithms, optimization of the deployment of methods, improvement in quality of image .

References
  1. R. W. Gerchberg, "Super-resolution through error energy reduction," Journal of Modern Optics, vol. 21, no. 9, pp. 709-720, 1974.
  2. P. D. Santis and F. Gori, "On an iterative method for super-resolution," Journal of Modern Optics, vol. 22, no. 8, pp. 691-695, 1975.
  3. F. Champagnat and G. L. Besnerais, "A Fourier inter-pretation of super-resolution techniques," Proceedingsof IEEE International Conference on Image Processing, vol. 1, pp. 865-868, Italy, 2005.
  4. R. Tsai and T. Huang, "Multiframe image restoration and registration," In R. Y. Tsai and T. S. Huang, editors,Advances in Computer Vision and Image Processing, vol. 1, pp. 317-339, JAI Press Inc. , 1984.
  5. V. H. Patil, D. S. Bormane, V. S. Pawar, "Super-resolution using neural network," Proceedings of IEEE2nd Asia International Conference on Modeling and Sim-ulation, Malaysia, 2008
  6. H. Demirel and G. Anbarjafari, "Image resolution en- hancement by using discrete and stationary wavelet de-composition," IEEE Transactions on Image Processing vol. 20, no. 5, pp. 1458-1460, 2011.
  7. S. Zhao, H. Han, and S. Peng, "Wavelet-domain HMT- based image super-resolution," Proceedings of IEEE In-ternational Conference on Image Processing, vol. 2, pp. 953-656, Spain, 2003.
  8. N. K. Bose, S. Lertrattanapanich, and M. B. Chappali "Super-resolution with second generation wavelets," Sig-nal Processing Image Communication, vol. 19, pp. 387-391, 2004.
  9. M. Chappalli and N. Bose, "Simultaneous noise _ltering and super-resolution with second-generation wavelets,"Signal Processing Letters, vol. 12, pp. 772-775, 2005.
  10. H. E. Sankaran, A. Gotchev, and K. Egiazarian, "Ef-_cient super-resolution reconstruction for translationalmotion using a near least squares resampling method,"Proceedings of IEEE International Conference on ImageProcessing, pp. 1745-1748, USA, 2006.
  11. M. Iiyama, K. Kakusho, and M. Minoh, "Super-resolution texture mapping from multiple view im-ages," Proceedings of International Conference on Pat-tern Recognition, pp. 1820-1823, Turkey, 2010.
  12. C. V. Jiji, M. V. Joshi, and S. Chaudhuri, "Single-frame image super-resolution using learned wavelet coef-_cients," International Journal of Imaging Systems and Technology, vol. 14, no. 3, pp. 105-112, 2004.
  13. S. Lui, J. Wu, H. Mao, and J. J. Lien, "Learning-based super-resolution system using single facial imageand multi-resolution wavelet synthesis," Proceedings of Asian Conference on Computer Vision, vol. 4884, pp. 96-105, Japan, 2007.
  14. F. Li, X. Jia, and D. Fraser, "Universal HMT basedsuper resolution for remote sensing images," Proceedingsof IEEE International Conference on Image Processing, pp. 333-336, USA, 2008.
  15. H. Ji and C. Fermuller, "Robust wavelet-based super-resolution reconstruction: theory and algorithm," IEEETransactions on Pattern Analysis and Machine Intelli-gence, vol. 31, no. 4, pp. 649-660, 2009.
  16. N. Fan, "Super-resolution using regularized orthogonal matching Pursuit based on compressed sensing theory inthe wavelet domain," Proceedings of International Con- ference on Computer Graphics, Imaging and Visualiza-tion, pp. 349-354, China, 2009.
  17. P. Sen, S. Darabi, "Compressive image super- resolution," Proceedings of 43rd IEEE Asilomar Confer-ence on Signals, Systems and Computers, pp. 1235-1242, USA, 2009.
  18. H. Shen, S. Li, "Hallucinating faces by interpolation and principal component analysis," Proceedings of Inter-national Symposium on Computational Intelligence and Design, pp. 295-298, China, 2009.
  19. N. Fan, "Super-resolution using regularized orthogonal matching Pursuit based on compressed sensing theory inthe wavelet domain," Proceedings of International Con- ference on Computer Graphics, Imaging and Visualization, pp. 349-354, China, 2009.
  20. J. Abad, M. Vega, R. Molina, and A. K. Katsaggelos, "Parameter estimation in super-resolution image recon-struction problems," IEEE International Conference on Acoustic, Speech and Signal Processing, vol. 3, pp. 709-
  21. N. K. Bose, M. K. Ng, and A. C. Yau, "A fast algorithm for image super-resolution from blurred observations,"EURASIP Journal on Advances in Signal Processing, 14 pages, Article ID 35726, 2006.
  22. P. Vandewalle, L. Sbaiz, J. Vandewalle, and M. Vetterli, "Super-resolution from unregistered and totally aliasedsignals using subspace methods," IEEE Transactions on Image Processing, vol. 55, no. 7, pp. 3687-373, 2007.
  23. G. Zweig, "Super-resolution Fourier transform by op-timization, and ISAR imaging," IEE Proceedings onRadar, Sonar and Navigation pp. 247-252, 2003.
  24. F. Champagnat and G. L. Besnerais, "A Fourier inter-pretation of super-resolution techniques," Proceedingsof IEEE International Conference on Image Processing,vol. 1, pp. 865-868, Italy, 2005.
  25. M. K. Ng and A. C. Yau, "Super-resolution image restoration from blurred low-resolution images," Journalof Mathematical Imaging and Vision, vol. 23, no. 3, pp 367-378, 2005.
  26. N. K. Bose, M. K. Ng, and A. C. Yau, "A fast algorithm for image super-resolution from blurred observations,"EURASIP Journal on Advances in Signal Processing, 14 pages, Article ID 35726, 2006.
  27. P. Vandewalle, S. E. Susstrunk, and M. Vetterli, "Super-resolution images reconstructed from aliased images,"Proceedings of SPIE Conference on Visual Communi- cations and Image Processing, vol. 5150, pp. 1398-1405,Switzerland, 2003.
  28. T. Akgun, Y. Altunbasak, and R. M. Mersereau,"Super-resolution reconstruction of hyperspectralim-ages," IEEE Transactions on Image Processing, vol. 14,pp. 1860-1875, 2005.
  29. P B Chopade and P M Patil,"Design of dydic-integer coefficient based bi-orthogonal wavelet filters for image super-resolution using image sub-pixel image"ICTACTJournals,vol. 04,Issue 04,May 2014.
  30. H. Demirel and G. Anbarjafari, "Image resolution en- hancement by using discrete and stationary wavelet de-composition," IEEE Transactions on Image Processing vol. 20, no. 5, pp. 1458-1460, 2011.
  31. M. Chappalli and N. Bose, "Simultaneous noise _ltering and super-resolution with second-generation wavelets,"Signal Processing Letters, vol. 12, pp. 772-775, 2005.
  32. M. Irani and S. Peleg, "Super-resolution from image se- quences," Proceedings of IEEE International Conferenceon Pattern Recognition, pp. 115-120, USA, 1990.
  33. M. Irani and S. Peleg, "Improving Resolution by Image Registration," CVGIP: Graphical Models and Image Pro-cessing, vol. 53, pp. 231-239, 1991.
  34. M. Irani and S. Peleg, "Image sequence enhancement using multiple motions analysis," Proceedings of Inter-national Conference on Computer Vision and Pattern Recognition, pp. 216-222, 1992.
  35. M. Irani and S. Peleg, "Motion analysis for image enhancement: resolution, occlusion, and transparency,"Journal of Visual Communication and Image Represen- tation, vol. 4, pp. 324-335, 1993.
  36. P. Cheeseman, B. Kanefsky, R. Kraft, and J. Stutz, "Super-resolved surface econstruction from multiple im-ages," Technical Report FIA9412, NASA, 1994.
  37. D. Capel and A. Zisserman, "Automated mosaicing with super-resolution zoom," Proceedings of the InternationalConference on Computer Vision and Pattern Recogni-tion, pp. 885-891, 1998.
  38. M. Elad and Y. Hel-Or, "A fast super-resolution recon- struction algorithm for pure translational motion andcommon space-invariant blur," IEEE Transactions on Image Processing, vol. 10, no. 8, pp. 1187-1193, 2001.
  39. S. Farsiu, D. Robinson, M. Elad, P. Milanfar. 2004, "Fast and robust multi-frame super-resolution," IEEETransactions on Image Processing, vol. 13, no. 10, pp. 1327-1344, 2004.
  40. L. C. Pickup, D. P. Capel, S. J. Roberts, and A. Zisser- man, "Bayesian image super-resolution, continued," Neural Information Processing Systems, vol. 19, pp. 1089- 1096, 2006.
  41. P. Cheeseman, B. Kanefsky, R. Kraft, and J. Stutz, "Super-resolved surface reconstruction from multiple images," Technical Report FIA9412, NASA, 1994.
  42. Q. Wang, X. Tang, and H. Shum, "Patch based blind im-age super-resolution," proceedings of 10th InternationalConference on Computer Vision, vol. 1, pp. 709-716,China, 2005.
  43. D. Kong, M. Han, W. Xu, H. Tao, Y. Gong, "A con-ditional random _eld model for video super-resolution"Proceedings of IEEE International Conference on Pattern Recognition, China, 2006.
  44. E. Mjolsness, "Neural networks, pattern recognition, and _ngerprint hallucination," PhD thesis, California Insti-tute of Technology, 1985.
  46. . X. Li, K. M. Lam, G. Qiu, L. Shen, and S. Wang, "Example-based image super-resolution with class-speci_c predictors," Journal of Visual Communication and Image Representation, vol. 20, no. 5, pp. 312-322,2009.
  47. L. Tian, A. Suzuki, and H. Koike, "Task-oriented evalu-ation of super-resolution techniques," Proceedings of In-ternational Conference on Pattern Recognition, pp. 493-496, Turkey, 2010.
  48. M. Elad and A. Feuer, Super-resolution reconstruction of continuous image sequences," Proceedings of Interna-tional Conference on Image Processing, vol. 3, pp. 459-463, Japan, 1999.
  49. I. Begin and F. P. Ferrie, "comparison of super-resolution algorithms using image quality measures,"Proceedings of 3rd Canadian Conference on Computer and Robot Vision, pp. 72, Canada, 2006.
  50. A. R. Reibman, R. M. Bell, and S. Gray, "Quality assess-ment for super-resolution image enhancement," Proceedings of IEEE International Conference on Image Process-ing, pp. 2017-2020 , USA, 2006.
  51. Nasrollahi, K. , & Moeslund, T. B. (2014). Super- resolution: A comprehensive survey. Machine Vision &Applications, 25(6), 1423-1474. 10. 1007/s00138-014-0623-4
Index Terms

Computer Science
Information Sciences

Keywords

Single and multi frame superresolution Image registration wavelet transform Assessment of SR algorithms.

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