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A New Image Super-Resolution Restoration Algorithm

by Sayed A. Mohamed, A. S. El-Sherbeny, Ayman H. Nasr, A. K. Helmy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 10
Year of Publication: 2017
Authors: Sayed A. Mohamed, A. S. El-Sherbeny, Ayman H. Nasr, A. K. Helmy
10.5120/ijca2017915355

Sayed A. Mohamed, A. S. El-Sherbeny, Ayman H. Nasr, A. K. Helmy . A New Image Super-Resolution Restoration Algorithm. International Journal of Computer Applications. 173, 10 ( Sep 2017), 5-12. DOI=10.5120/ijca2017915355

@article{ 10.5120/ijca2017915355,
author = { Sayed A. Mohamed, A. S. El-Sherbeny, Ayman H. Nasr, A. K. Helmy },
title = { A New Image Super-Resolution Restoration Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 10 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 5-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number10/28443-2017915355/ },
doi = { 10.5120/ijca2017915355 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:57.282006+05:30
%A Sayed A. Mohamed
%A A. S. El-Sherbeny
%A Ayman H. Nasr
%A A. K. Helmy
%T A New Image Super-Resolution Restoration Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 10
%P 5-12
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a new image super-resolution restoration algorithm. The development of the algorithm is based on the improvement of the classical projection onto convex set (POCS) algorithm and the stationary wavelet transform (SWT) to restore a super-resolution image from Egyptsat-1 low resolution (LR) images. Egyptsat-1 bands have inconsistent sub-pixel shift. this inconsistent shift between the bands is changed into reliable shift by adaptive interpolation. Then, decomposition of high frequency sub-bands is generated using (SWT). The POCS iteration is used to restore high-resolution (HR) image from every LR wavelet decomposed images. The HR image is reconstructed by inverse wavelet transform. The result showed that the proposed method achieved significant spatial resolution improvements from 7.8 m to 4 m by using (POCS). The reconstructed image is evaluated by several quantitative measures: the peak signal-noise ratio(PSNR), root main square error(RMSE), entropy, and Objective Fusion Measure. These measures of the proposed method were also assessed and tested with some implemented commonly used SR methods. The experimental results of the processed Egyptsat-1 images showed that the proposed method can improve the ability of fusing different image information, and the visual and quantitative evaluations verify its usefulness and effectiveness.

References
  1. Nasr, A.H; Gh.S. El-Tawel; A.K. Helmy. "super resolution for egyptsat-1 images with erratic shift "Journal of Computer Science 10 (8): pp-1324-1335,ISSN:1549-3636, 2014
  2. S.A. Mohamed; A.K. Helmy; M.A. Fkirin; S.M. Badway" A Proposed Method to Measure Sub Pixel Shift in Egyptsat-1 Aliased Images"International Journal of Computer ApplicationsVolume 95– No. 10, June 2014
  3. T. Stathaki " Image Fusion: Algorithms and Applications". 1st ed. Amsterdam, The Netherlands: Elsevier. 2008
  4. Jifei Yu; Xinbo Gao; Dacheng Tao;Xuelong Li;Kaibing Zhang" A unified learning framework for single image super-resolution" IEEE transactions on neural networks and learning systems,volume:25 ,pp:780-792, issue:4, 2014
  5. Liu, Lixin; Bian, Hongyu; Shao, Guofeng" An effective wavelet-based scheme for multi-focus image fusion"IEEE International Conference on Mechatronics and Automation, pp:1720-1725, 2013
  6. Zitova B and Flusser J " Image Registration Methods: A Survey. Image and Vision Computing", 11(21), pp. 977–1000, 2003.
  7. Robinson D, Farsiu S and Milanfar P "Optimal Registration of Aliased Images Using Variable Projection with Application to Super-Resolution". The Computer Journal. 52, Issue 1, pp. 31–42, 2009.
  8. S.A. Mohamed; A.K. Helmi ; M.A. Fkirin; S.M. Badwai" Accuracy Analysis of Phase Correlation Shift Measurement Methods Applied to Egyptsat-1 Satellite" Radio Science Conference (NRSC), 2013 30th National ,ISBN 978-1-4673-6219-1,PP: 347-358,Cairo,Egypt ,16-18 April 2013
  9. S.A. Mohamed; A.K. Helmi ; M.A. Fkirin; S.M. Badwai, "Subpixel Accuracy Analysis of Phase Correlation Shift Measurement Methods Applied to Satellite Imagery"(IJACSA) International Journal of Advanced Computer Science and Applications, ISSN21565570,Vol. 3, No. 12, pp. 202-206, 2012.
  10. Starck J L and Pantin E"Multiscale maximum entropy image restoration". Vistas Astron. 40, pp. 563–569, 1996.
  11. Bernd Jähne, "Digital image processing" 6th Edition, ISBN 3-540-24035-7 Springer Berlin, Heidelberg, New York, 2005.
  12. Steven T. Karris, "Signals and Systems with MATLAB Applications, Second Edition", Orchard Publications, ISBN 0-9709511-8-3, 2003.
  13. Van Dewalle P"Super-resolution from Unregistered Aliased Images". Thèse No. 3591 école polytechnique fédérale de Lausanne pour l'obtention du grade de docteur ès sciences, 2006.
  14. Agrawal, Mayank Dash, Ratnakar" Image Resolution Enhancement Using Lifting Wavelet and Stationary Wavelet Transform" 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologiespp- 322-325, 2014
  15. Deng, Juan Ban, Yifang Liu, Jinshuo Li, LiNiu, XinZou, Bin" Hierarchical Segmentation of Multitemporal RADARSAT-2 SAR Data Using Stationary Wavelet Transform and Algebraic Multigrid Method" IEEE Transactions on Geoscience and Remote Sensing,pp- 4353-4363,vol52 issue:7,2014
  16. J. E. Fowler, “The redundant discrete wavelet transform and additive noise,”Mississippi State ERC, Mississippi State University, Tech. Rep. MSSU-COE-ERC-04-04, Mar. 2004
  17. Demirel, Hasan; Anbarjafari, Gholamreza" IMAGE resolution enhancement by using discrete and stationary wavelet decomposition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society,2011
  18. Deepali A.Godse, Dattatraya S. Bormane “Wavelet based image fusion using pixel based maximum selection rule”International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462, Vol. 3 No. 7 July 2011
  19. Gonzalo Pajares , Jesus Manuel de la Cruz “A wavelet-based image fusion tutorial” Pattern Recognition Society, 2004.
  20. G. P. Nason and B. W. Silverman-The stationary wavelet transform and some statistical applications in wavelet and statistics. In: Antoniadis A ed. Lecture Notes in Statistics. Berlin: Spinger Verlag, 281-299, 1995.
  21. Chaudhary, Manoj D. Upadhyay, Abhay B." Fusion of local and global features using Stationary Wavelet Transform for efficient Content Based Image Retrieval".2014 IEEE Students' Conference on Electrical, Electronics and Computer Science,pp-1-6,2014
  22. Pires, Rafael G. Pereira, Luis a. M. Mansano, Alex F. Papa, Joao P." A hybrid image restoration algorithm based on Projections Onto Convex Sets and Harmony Search" 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013)pp-2824-2827,issue:3,2013
  23. Xi, Huiqin Xiao, Chuangbai Bian, Chunxiao." Edge Halo Reduction for Projections onto Convex Sets Super Resolution Image Reconstruction" 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA),pp-1-7,2012
  24. H. Stark, Y. Yang, and Y. Yang, Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics. John Wiley & Sons, 1998
  25. D. C. Youlq “Generalized image restoration by the method of alternating orthogonal projections,” IEEE Trans. Circuiis Syst., vol. CAS-25, pp.694-702, 1978.
  26. H. I. Trussell and M. R. Civanlar, “Feasible solution in signal restoration,” IEEE Trans. Acousi., Speech, Signal Processing, vol. ASSP-32, pp. 201-212, 1984
  27. A.J. Patti, M. I. Sezan, and A. M Tekalp, “Superresolution Video ReconstNction with Arbitrary Sampling Lattices and Nonzero Aperture Time”, IEEE Trans. Image Processing, vol. 6, no. 8, pp1646-1658, 1997
  28. A. Enis Cetin, Alican Bozkurt, Osman Gunay, Y. Hakan Habiboglu, Kivanc Kose, Ibrahim Onaran, R. A. Sevimli." Projections Onto Convex Sets (POCS) Based Optimization by Lifting" pp-4799 ,Issue:4 1st IEEE Global Conference on Signal and Information Processing (GLOBALSIP)", 2013
  29. H. Stark, P. Oskoui, "High-resolution image recovery from image plane arrays using convex projections," Journal of the Optical Society of America A. vol. 6, pp. 1715-1726, 1989.
  30. J. P. Papa, L. M. G. Fonseca, and L. A. S. de Carvalho, "Projections onto convex sets through particle swarm optimization and its application for remote sensing image restoration," Pattern Recognition Letters. vol. 31, pp. 1876-1886, 2010
  31. P. E. Eren, M. I. Sezan, and A. M. Tekalp, "Robust, object-based high- resolution image reconstruction from low-resolution video," IEEE Transactions on Image Processing. vol.10, pp. 1446-1451, 1997
  32. J. Patti, M. I. Sezan, and A. M. Tekalp, "Super-resolution video re- construction with arbitrary sampling lattices and nonzero aperture time," IEEE Transactions on Image Processing. vol.8, pp.1064-1076, 1997
  33. A. J. Patti, Y. Altunbasak, "Artifact reduction for set theoretic super resolution image reconstruction with edge adaptive constraints and higher-order interpolants," IEEE Transactions on Image Processing. vol. 10, pp. 179-186, 2001.
  34. T. Ogawa, M. Haseyama, "Missing intensity interpolation using a kernel PCA-based POCS algorithm and its applications," IEEE Transactions on Image Processing. vol. 20, pp. 417-432, 2011
  35. S. Gho, C. Liu, and D. Kim. "Application of low-pass & high-pass reconstruction for improving the performance of the POCS based algorithm,"in 2011 IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS). Korea: Seoul, pp. 1-3, 2011
  36. Huiqin Xi,Xiao, Chuangbai Xiao, Chunxiao Bian"Edge Halo Reduction for Projections onto Convex Sets Super Resolution Image Reconstruction"2012 International Conference on Digital Image Computing Techniques and Applications (DICTA) ,pp:1-7 , 2012
  37. Nelson.l;Avideh zakhor" Constructing a Multivalued Representation for View Synthesis" International Journal of Computer Vision, Volume 45 Issue 2, Pages 157 - 190, November2001
  38. Marcia L. S. Aguena, Nelson D. A. . Mascarenhas"  Multispectral image data fusion using POCS and super-resolution"  Volume 102 Issue 2,Pages 178-187. Elsevier Science Inc. New York, May 2006.
  39. Hsu, J.T; Sclabassi, R.J." A wavelet constrained POCS supper-resolution algorithm for high resolution image reconstruction from video sequence" International Conference on Neural Networks and Signal Processing,vol:2,pp:1266-1269, Proceedings of the 2003
  40. A. Kolokolov.,G. Tarasov.,S. Medvednikone.,V. Gladilin.,"Egyptsat-1 satellite specification" NARSS technology transfer documents., Issue 3, pp13-26, 2006.
  41. M .Chandana,S. Amutha, and Naveen Kumar, “ A Hybrid Multi-focus Medical Image Fusion Based on Wavelet Transform”. International Journal of Research and Reviews in Computer Science (IJRRCS) Vol. 2, No. 4, ISSN: 2079- 2557, August 2011
  42. C.S. Xydeas and V. Petrovid" Objective image fusion performance measure" ELECTRONICS LETTERS 17th February 2000 Vol. 36 No. 4
  43. Bannore V "Iterative-interpolation super-resolution image reconstruction: A computationally efficient technique. Springer, Berlin, 2009.
  44. Panda S S, Prasad M S R S, and Jena G "POCS based Super-resolution image reconstruction using an adaptive regularization parameter". IJCSI International Journal of Computer Science, 8, Issue 5, No. 2, 2011.
  45. Irani M and Peleg S "Improving resolution by image registration. Graphical Models and Image Processing", 53, pp. 231-239, 1991.
  46. Zomet A, Rav-Acha A and Peleg S "Robust Super-Resolution". Proc. of the International Conference on Computer Vision and Pattern Recognition (CVPR), 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Super-resolution Projection on convex set Stationary wavelet transforms Egyptsat-1 images Peak signal-noise ratio Root main square error Entropy Objective fusion measure.

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