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Reseach Article

A Survey on Super-Resolution Methods for Image Reconstruction

by Elham Karimi, Kaveh Kangarloo, Shahram Javadi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 3
Year of Publication: 2014
Authors: Elham Karimi, Kaveh Kangarloo, Shahram Javadi
10.5120/15557-4300

Elham Karimi, Kaveh Kangarloo, Shahram Javadi . A Survey on Super-Resolution Methods for Image Reconstruction. International Journal of Computer Applications. 90, 3 ( March 2014), 32-39. DOI=10.5120/15557-4300

@article{ 10.5120/15557-4300,
author = { Elham Karimi, Kaveh Kangarloo, Shahram Javadi },
title = { A Survey on Super-Resolution Methods for Image Reconstruction },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 3 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number3/15557-4300/ },
doi = { 10.5120/15557-4300 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:08.762109+05:30
%A Elham Karimi
%A Kaveh Kangarloo
%A Shahram Javadi
%T A Survey on Super-Resolution Methods for Image Reconstruction
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 3
%P 32-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today, in many applications of Machine vision, image Super-Resolution is preferred. Super-Resolution is estimation of a high-resolution image from an image or several low resolution images. Popular techniques in the field of enhancing images can be used to remove noise or blurring. In this paper, an overview of super resolution methods has been presented. Types of resolution methods have been used so far can be divided into three groups as frequency-domain methods, spatial domain methods and techniques can be classified as the wavelet domain. Super-resolution methods in different domains have different characteristics and comparison between these methods is usually done using a special index in one domain. In this paper, we will introduce these indexes and review best techniques used in all three domains.

References
  1. Sun Hao; Luo Lin; Zhou Weiping; Luo Limin, "Location and Super-Resolution Enhancement of License Plates Based on Video Sequences," Information Science and Engineering (ICISE), 2009 1st International Conference on , vol. , no. , pp. 1319,1322, 26-28 Dec. 2009.
  2. Hong Yu,; Ma Xiang,; Huang Hua,; Qi Chun,, "Face image super-resolution through POCS and residue compensation," Visual Information Engineering, 2008. VIE 2008. 5th International Conference on , vol. , no. , pp. 494,497, July 29 2008-Aug. 1 2008.
  3. Marco Crisani, Dong Seon Cheng, Vittorio Murino, and Donato Pannullo. "Distilling information with super-resolution for video surveillance". In Proceedings of the ACM 2nd International Workshop on Video Surveillance and Sensor Networks, pages 2–11, 2004.
  4. Frank Lin, Clinton B. Fookes, Vinod Chandran, and Sridha Sridharan. "Investigation into optical flow super-resolution for surveillance applications". In The Austrilian Pattern Recognition Society Worshop on Digital Image Computing, 2005.
  5. Feng Li; Xiuping Jia; Fraser, D. , "Universal HMT based super resolution for remote sensing images," Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on , vol. , no. , pp. 333,336, 12-15 Oct. 2008.
  6. J. Maintz and M. Viergever. A survey of medical image registration. Medical Image Analysis, Vol. 2, No. 1. , pp. 1-36, March 1998 .
  7. Roohi, S. ; Zamani, J. ; Noorhosseini, M. ; Rahmati, M. , "Super-resolution MRI images using Compressive Sensing," Electrical Engineering (ICEE), 2012 20th Iranian Conference on , vol. , no. , pp. 1618,1622, 15-17 May 2012.
  8. Schultz, R. R. ; Stevenson, R. L. , "Extraction of high-resolution frames from video sequences," Image Processing, IEEE Transactions on , vol. 5, no. 6, pp. 996,1011, Jun 1996.
  9. Yun Zhang; Mishra, R. K. , "A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion," Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International , vol. , no. , pp. 182,185, 22-27 July 2012.
  10. Sheppard, D. G. ; Hunt, B. R. ; Marcellin, M. W. , "Iterative multiframe super-resolution algorithms for atmospheric turbulence-degraded imagery," Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on , vol. 5, no. , pp. 2857,2860 vol. 5, 12-15 May 1998.
  11. Suvrajit Maji, "Generative Models for Super-Resolution Single Molecule Microscopy Images of Biological Structures", Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh PA 15213, CMU-CB-12-104, August 2012.
  12. Kunter, M. ; Jangheon Kim; Sikora, T. , "Super-resolution Mosaicing using Embedded Hybrid Recursive Folow-based Segmentation," Information, Communications and Signal Processing, 2005 Fifth International Conference on , vol. , no. , pp. 1297,1301, 0-0 0.
  13. 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, volume 1, pages 317{339, JAI Press Inc. (1984).
  14. Kaltenbacher, E. ; Hardie, R. C. , "High resolution infrared image reconstruction using multiple, low resolution, aliased frames," Aerospace and Electronics Conference, 1996. NAECON 1996. , Proceedings of the IEEE 1996 National , vol. 2, no. , pp. 702,709 vol. 2, 20-23 May 1996 .
  15. Tekalp, A. M. ; Ozkan, M. K. ; Sezan, M. I. , "High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration," Acoustics, Speech, and Signal Processing, 1992. ICASSP-92. , 1992 IEEE International Conference on , vol. 3, no. , pp. 169,172 vol. 3, 23-26 Mar 1992.
  16. R. Sudheer Babu, Dr. K. E. Sreenivasa Murthy, "A Survey on the Methods of Super-Resolution Image Reconstruction", International Journal of Computer Applications (0975 – 8887), Volume 15– No. 2, February 2011.
  17. Kim, S. P. ; Bose, N. K. ; Valenzuela, H. M. , "Recursive reconstruction of high resolution image from noisy undersampled multiframes," Acoustics, Speech and Signal Processing, IEEE Transactions on , vol. 38, no. 6, pp. 1013,1027, Jun 1990.
  18. Kim, S. P. ; Su, W. -Y. , "Recursive high-resolution reconstruction of blurred multiframe images," Acoustics, Speech, and Signal Processing, 1991. ICASSP-91. , 1991 International Conference on , vol. , no. , pp. 2977,2980 vol. 4, 14-17 Apr 1991.
  19. Kim, S. P. ; Su, W. -Y. , "Recursive high-resolution reconstruction of blurred multiframe images," Image Processing, IEEE Transactions on , vol. 2, no. 4, pp. 534,539, Oct 1993.
  20. Bose, N. K. ; Kim, H. C. ; Valenzuela, H. M. , "Recursive implementation of total least squares algorithm for image reconstruction from noisy, undersampled multiframes," Acoustics, Speech, and Signal Processing, 1993. ICASSP-93. , 1993 IEEE International Conference on , vol. 5, no. , pp. 269,272 vol. 5, 27-30 April 1993.
  21. Bose, N. K. ; Kim, H. C. ; Zhou, B. , "Performance analysis of the TLS algorithm for image reconstruction from a sequence of undersampled noisy and blurred frames," Image Processing, 1994. Proceedings. ICIP-94. , IEEE International Conference , vol. 3, no. , pp. 571,574 vol. 3, 13-16 Nov 1994.
  22. H. Ur and D. Gross, "Improved resolution from subpixel shifted pictures". CVGIP: Graphical Models and Image Processing, 54: 181{186 (March 1992).
  23. Sean Borman, Robert Stevenson, "Spatial Resolution Enhancement of Low-Resolution Image Sequences, A Comprehensive Review with Directions for Future Research," Laboratory for Image and Signal Analysis (LISA), University of Notre Dame, NotrebDame, IN 46556, July 8, 1998.
  24. M. Irani and S. Peleg, " Motion analysis for image enhancement:Resolution, occlusion and transparency," Journal of Visual Communications and Image Representation, vol. 4, issue4, pages 324-335, December1993.
  25. M. Irani, S. Peleg. , "Super resolution from image sequences," Pattern Recognition, 1990. Proceedings. , 10th International Conference on , vol. ii, no. , pp. 115,120 vol. 2, 16-21 Jun 1990.
  26. M. Irani and S. Peleg," Improving resolution by image registration". CVGIP: Graphical Models and Imaging Processing, vol53, issue3,pages 231-239, May 1991.
  27. Tom, B. C. ; Katsaggelos, A. K. ; Galatsanos, N. P. , "Reconstruction of a high resolution image from registration and restoration of low resolution images," Image Processing, 1994. Proceedings. ICIP-94. , IEEE International Conference , vol. 3, no. , pp. 553,557 vol. 3, 13-16 Nov 1994.
  28. Capel, D. ; Zisserman, A. , "Computer vision applied to super resolution," Signal Processing Magazine, IEEE , vol. 20, no. 3, pp. 75,86, May 2003.
  29. K. M. Hanson and G. W. Wecksung,"Bayesian approach to limited-angle reconstruction in computed tomography," Journal of Optical Society of America, JOSA, Vol. 73, Issue 11, pp. 1501-1509 (1983).
  30. L. Rudin, S. Osher, and E. Fatemi. "Nonlinear total variation based noiseremoval algorithms," Journal Physica D, Nonlinear Phenomena, Volume 60, Issue 1-4, Nov. 1, Pages 259-268, 1992.
  31. N. Nguyen, P. Milanfar, and G. H. Golub. "A computationally efficient image superresolution algorithm," IEEE Transactions on Image Processing, 10(5):573–583, 2001. .
  32. Tom, B. C. ; Katsaggelos, A. K. , "Reconstruction of a high-resolution image by simultaneous registration, restoration, and interpolation of low-resolution images," Image Processing, 1995. Proceedings. , International Conference on , vol. 2, no. , pp. 539,542 vol. 2, 23-26 Oct 1995.
  33. Hardie, R. C. ; Barnard, K. J. ; Armstrong, E. E. , "Joint MAP registration and high-resolution image estimation using a sequence of undersampled images," Image Processing, IEEE Transactions on , vol. 6, no. 12, pp. 1621,1633, Dec 1997.
  34. Michael E. Tipping and Christopher M. Bishop. "Bayesian image superresolution," In Proceedings of Advances in Neural Information ProceedingSystems, pages 1279–1286, 2003.
  35. Efros, A. A. ; Leung, T. K. , "Texture synthesis by non-parametric sampling," Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on , vol. 2, no. , pp. 1033,1038, 1999.
  36. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. H. Salesin. "Image analogies". In Proceedings of the 28th annual conference Computer Graphics and Interactive Techniques, pages 327–340, 2001.
  37. Freeman, W. T. ; Jones, T. R. ; Pasztor, E. C. , "Example-based super-resolution," Computer Graphics and Applications, IEEE , vol. 22, no. 2, pp. 56,65, Mar/Apr 2002.
  38. Patti, A. J. ; Sezan, M. I. ; Tekalp, A. M. , "High-resolution image reconstruction from a low-resolution image sequence in the presence of time-varying motion blur," Image Processing, 1994. Proceedings. ICIP-94. , IEEE International Conference , vol. 1, no. , pp. 343,347 vol. 1, 13-16 Nov 1994.
  39. Elad, M. ; Feuer, A. , "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, Dec 1997.
  40. Patti, A. J. ; Tekalp, A. M. ; Sezan, M. I. , "A new motion-compensated reduced-order model Kalman filter for space-varying restoration of progressive and interlaced video," Image Processing, IEEE Transactions on , vol. 7, no. 4, pp. 543,554, Apr 1998.
  41. G. Crist´obala, E. Gila, F. ?Sroubekb, J. Flusserb, C. Miravetc, F. B. Rodr´?guezc. "Superresolution imaging: a survey of current techniques," Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 70740C September 03, 2008.
  42. T. Komatsu, T. Igarashi, K. Aizawa, and T. Saito. "Very high resolution imaging scheme with multiple different aperture cameras". Signal Processing Image Communication,vol 5,issue5-6,pages 511– 526, Dec 1993.
  43. Jing Tian, Kai-Kuang Ma, "A survey on super-resolution imaging," Signal, Image and Video Processing In Signal, Image and Video Processing, Vol. 5, No. 3, pp. 329-342 3 February 2011.
  44. A. Gilman and D. G. Bailey, "Near optimal non-uniform interpolation for image super-resolution from multiple images", in Image and Vision Computing New Zealand (IVCNZ'06), Great Barrier Island, NZ, pp 31-36 (27-29 November, 2006).
  45. M. S. Alam, J. G. Bognar, R. C. Hardie, and B. J. Yasuda. Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames. IEEE Transactions on Instrumentation and Measurement, 49(5):915–923, 2000.
  46. Anders Ohman, "Methods and algorithms for image fusion and super resoloution ," Master of science thesis,department signals and systems,chalmers university of technology,2009.
  47. Heechang Kim; Sangjun Park; Jin Wang; Yonghoon Kim; Jechang Jeong, "Advanced Bilinear Image Interpolation Based on Edge Features," Advances in Multimedia, 2009. MMEDIA '09. First International Conference on , vol. , no. , pp. 33,36, 20-25 July 2009.
  48. Keys, R. , "Cubic convolution interpolation for digital image processing," Acoustics, Speech and Signal Processing, IEEE Transactions on , vol. 29, no. 6, pp. 1153,1160, Dec 1981.
  49. Bagawade Ramdas P. , Bhagawat Keshav S. , Patil Pradeep M. , "Wavelet transform techniques for image resolution enhancement: a study", International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 4, April 2012.
  50. Hasan Demirel and Gholamreza Anbarjafari, "Image Super Resolution Based on Interpolation of Wavelet Domain High Frequency Subbands and the Spatial Domain Input Image,", ETRI Journal, Volume 32, Number 3, June 2010.
  51. H. Y. LIU, Y. S. ZHANG, Song JI, "Sudy on the methods of super-resolution image reconstruction",The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B2. Beijing 2008.
  52. S. H. M. Allon, M. G. Debertran, B. T. H. M. Sleutjes, "Fast Deblurring Algorithms," pp. 1-25, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

super-resolution noise elimination blurring frequency-domain spatial domain wavelet domain.