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Image Super-resolution with Improved Wiener Restoration and Simultaneous Edge Enhancement

by Nisha. B. R, Priya. S, Ashok Kumar. T
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 12
Year of Publication: 2014
Authors: Nisha. B. R, Priya. S, Ashok Kumar. T
10.5120/17575-7993

Nisha. B. R, Priya. S, Ashok Kumar. T . Image Super-resolution with Improved Wiener Restoration and Simultaneous Edge Enhancement. International Journal of Computer Applications. 100, 12 ( August 2014), 5-12. DOI=10.5120/17575-7993

@article{ 10.5120/17575-7993,
author = { Nisha. B. R, Priya. S, Ashok Kumar. T },
title = { Image Super-resolution with Improved Wiener Restoration and Simultaneous Edge Enhancement },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 12 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number12/17575-7993/ },
doi = { 10.5120/17575-7993 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:46.115074+05:30
%A Nisha. B. R
%A Priya. S
%A Ashok Kumar. T
%T Image Super-resolution with Improved Wiener Restoration and Simultaneous Edge Enhancement
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 12
%P 5-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Super resolution is the process of increasing the spatial resolution of an image by exploiting additional sub pixel information from frames of input image sequences. Proposed method focuses on multi frame, spatial domain and reconstruction based super resolution techniques, namely, non uniform interpolation and iterated back projection. The performances of these two methods are improved by the addition of a restoration technique using improved Wiener restoration. Visual appearance of the image is further increased by iterative median filter that sharpens the edges. Results of the two methods are compared using a quality metric, peak signal to noise ratio (PSNR).

References
  1. M. K Park S. C Park and M. G Kang. Super-resolution Image Reconstruction: A Technical Overview. IEEE Signal Processing Magazine, 20:21–36, May 2003.
  2. S. Borman and R. L. Stevenson, Super-resolution from image sequences: A review. Proceedings, Midwest Symp, Circuits and Systems, pp. 374-378, 1999.
  3. Sean Borman and Robert Stevenson, Spatial Resolution Enhance ment of Low Resolution Image Sequences - A Comprehensive Review with Directions for Future Research , Opt. Eng. , July 1998.
  4. David Peter Capel, Image Mosaicing and Super-Resolution. In Proceedings of the 10th International Conference on Pattern Recognition, Atlantic City, NJ, volume 2, pages 115-120,June1990.
  5. Daniel Glasner , Shai Bagon & Michal Irani, Super Resolution from a Single Image, IEEE transactions, 2000.
  6. S. Farsiu, D. Robinson, M. Elad, P. Milanfar. Fast and Robust multi frame super resolution, IEEE transactions on Image Processing, Vol 13, pp 1327 - 1344, 2004
  7. T. S. Huang and R. Y. Tsai, Multi-frame image restoration and registration, Advances in Computer Vision and Image Process. , vol. 1, pp. 317-339, 1984.
  8. Patrick Vandewalle, Sabine Sstrunck and Martin Vetterli. Super Resolution Images Reconstructed from Aliased images, IEEE Transactions 1998
  9. Sanket. B. Kasturiwala, S. A. Ladakhe, A spatial domain Super-resolution approach for Soybean Leaf deceased image. vol. 3, Issue 2, pp. 97-106, 2013.
  10. T. R Jones, W. T Freeman and E. C Pasztor, Example based super resolution. In IEEE Transactions on Computer Graphics and applications, volume 22, pages 56-65, April 2002.
  11. Kyaha Choi, Changhyun Kim and Jong Beom Ra, Example based superresolution via structure analysis of patches. In IEEE Signal Processing Letters, volume 20, pages 56-65, April 2013.
  12. D. Keren, S. Peleg and R, Brada, Image sequence enhancement using sub pixel displacements, Proceedings of IEEE Computer Society Conference On Computer Vision and Pattern Recognition (CVPR 88), pp 742-746, USA, June 1988.
  13. M. Irani and S. Peleg, Improving resolution by image registration, In CVGIP: Graphical Models and Image Processing, volume 53, pages 231-239, May 1991.
  14. Capel. D and Zisserman. A, Computer vision applied to super-resolution. IEEE Signal Processing: Image Communication, 5, 511-526,2003
  15. Rajan D, Chaudhari Sand Joshi M V, Multi Objective super-resolution: Concepts and examples, IEEE Signal Processing Magazine, vol 20, pp 49-61, 2003.
  16. H. Stark and P. Oskoui, High resolution image recovery from image plane arrays, using convex projections, J. Opt. Soc. Am. A, vol. 6, pp. 1715-1726, 1989.
  17. C. Fan, J. Zhu, J. Gong, and C. Kuang. POCS super-resolution sequence image reconstruction based on improvement approach of Keren registration method, in Proc. 6th Int. Conf. ISDA, pp 333-337,Oct 2006.
  18. G. Chantas, N. Galatsanos, and N. Woods. , Super-resolution based on fast registration and maximum a posteriori reconstruction, IEEE Trans. Image Process. , vol. 16, no. 7, pp. 1821-1830, Jul. 2007.
  19. S. Belekos, N. Galatsanos, and A. Katsaggelos, Maximum a posteriori video super-resolution using a new multichannel image prior, IEEE Trans. Image Process. , vol. 19, no. 6, pp. 1451-1464, June2010.
  20. H. Ji and C. Fermuller, Robust wavelet based super-resolution reconstruction, Theory and algorithm, IEEE Trans, Pattern Analysis, Intell vol 31, no 4, pp 649-660, April 2009
  21. M. Irani & S. Peleg. Motion analysis for image enhancement resolution, occlusion and image representation, Vol 4, pp 324 335, 1993.
  22. Carman Neustaedter. An Evaluation of Optical Flow using Lucas and Kanades algorithm, CVPR,2002.
  23. B. Marcel, M. Briot and R. Murrieta, Calcul de translation et rotation par la transformation de Fourier, Traitement du Signal, 14(2), pp 135- 149, 1997.
  24. L. Luchesse and M. Cortelazzo, A noise robust frequency domain technique for estimating planar roto-translation, IEEE transactions in Signal Processing, vol 48, pp 1769-1786, June 2000.
  25. Martin A. Fischler, Robert C. Bolles, Random Sample Consensus: A paradigm for Model fitting with applications to Image analysis and automated cartography, Comm of the ACM, vol 24(6), pp 381-395, June 1981.
  26. D. Sun, S. Roth, and M J Black, Secrets of optical flow estimation and their principles, In IEEE International Conference on Computer Vision and Pattern Recognition, 2010.
  27. S. baker and T. Kanade, Super-resolution optical flow, Technical report, CMU, 1999.
  28. Russel Hardie. A Fast Image Super-Resolution Algorithm Using an Adaptive Wiener Filter, IEEE Transactions On Image Processing, Vol 16, No 12, December 2007.
  29. Elad, M. and Feuer, A. Restoration of single super-resolution image from several blurred, noisy and down-sampled measured images. IEEE Trans- actions on Image Processing, Issuee 6, Vol 12, pp 164658, 1997.
  30. A. Cheref & C. Serief. Deblurring And Denoising with Edge Enhancement of Satellite Images Using Super Resolution Tech- niques, 2006
  31. M. V. W. Zibetti & J. Mayer. Outlier Robust and edge preserving simultaneous Super Resolution, In Proceedings of IEEE Interna tional Conference on Image Processing, pp 1741 - 1744, 2006
  32. C. Tomasi and R. Manduchi, Bilateral filtering for gray and color images, IEEE International Conference Computer Vision, New Delhi, India, pp 836-846, January 1998.
  33. M. Elad, On the Bilateral filter and ways to improve it, IEEE International Transactions Image Processing vol 11, pp 1141-1151, October 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Super resolution motion estimation non uniform interpolation iterated back projection Wiener filter iterative median filter

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