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Image Inpainting – An Inclusive Review of the Underlying Algorithm and Comparative Study of the Associated Techniques

by Mahroosh Banday, Richa Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 17
Year of Publication: 2014
Authors: Mahroosh Banday, Richa Sharma
10.5120/17274-7699

Mahroosh Banday, Richa Sharma . Image Inpainting – An Inclusive Review of the Underlying Algorithm and Comparative Study of the Associated Techniques. International Journal of Computer Applications. 98, 17 ( July 2014), 10-20. DOI=10.5120/17274-7699

@article{ 10.5120/17274-7699,
author = { Mahroosh Banday, Richa Sharma },
title = { Image Inpainting – An Inclusive Review of the Underlying Algorithm and Comparative Study of the Associated Techniques },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 17 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 10-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number17/17274-7699/ },
doi = { 10.5120/17274-7699 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:26:26.070076+05:30
%A Mahroosh Banday
%A Richa Sharma
%T Image Inpainting – An Inclusive Review of the Underlying Algorithm and Comparative Study of the Associated Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 17
%P 10-20
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image In-painting, the technique that aims to revert deterioration (scratches, artifacts in photographs and videos) in images in an undetectable form, is as ancient as artistic creation itself. Digital Image In painting, a relatively young research area is an art of filling in the missing or corrupted regions in an image using information from the neighbouring pixels in a visually plausible manner, while restoring its unity. In painting which is essentially an image interpolation problem has numerous applications. It is helpfully used for object removal in digital photographs, image reconstruction, text removal, video restoration, special effects in movies disocclusion and so on. Several approaches have been proposed by the researchers to correct the occlusion. This proposed work presents a comparative study to provide a comprehensive visualization of different image in painting techniques. In this paper different types of image in painting algorithms are placed in juxtaposition. The algorithms are analysed theoretically as well as experimentally, based on which a ranking of algorithms will be established over different kinds of applications in diverse areas .

References
  1. Criminisi, A. , P´erez, P. and Toyama, K. (2003). Object Removal by Exemplar-Based Inpainting, Proceedings of the 2003, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp:1-8.
  2. Criminisi,A. . , Perez, P. and Toyama, K. (Sep 2004). Region Filling and Object Removal by Exemplar based Image In-Painting, IEEE Trans. on Image Processing, 13, pp: 1200-1212.
  3. Efors, A. and Leung, T. K. (1999). Texture synthesis by non-parametric sampling, Proceedings of the 17th IEEE International Confere on Computer Vision, pp: 1033-1038.
  4. Alexandra Ioana Oncu Feier. (2012). Digital Inpainting for Artwork Restoration: Algorithms and Evaluation, Master Thesis Report.
  5. Fang C. W and Lien,J. J. J. Fast image replacement using multi-resolution approach
  6. Christine Guillemot and Olivier Le Meur. 2014. Image Inpainting- Overview and recent advances, IEEE signal processing magazine,pp:127-144,ISSN: 1053-5888.
  7. Bornemann, F. and Marz,T. (July 2007). Fast image inpainting based on coherence transport, J. Math. Imaging Vis,28(3), pp:259–278.
  8. Emile-Male,G. (1976). The Restorer's Handbook of Easel Painting, Van Nostrand Reinhold, New York.
  9. Yamauchi,H. , Haber,J. , Seidel, H. P. (2003). Image Restoration Using Multiresolution Texture Synthesis and Image Inpainting, Computer Graphics International, pp:108-113.
  10. Drori,I. , Cohen-Or,D. and Yeshurun,D. (2003). Fragment-based image completion, ACM Transactions on Graphics, 22, pp: 303-312.
  11. Jia,J. and Tang,C. K. (2003). Image repairing: Robust image synthesis by adaptive and tensor voting, Proceedings of IEEE Computer Society Conference on Computer Vision Pattern Recognition, pp: 643-650.
  12. Shen,J. (June 2003). Inpainting and the Fundamental Problem of Image Processing, SIAM News, 36(5).
  13. Zhou,J. and . Kelly,A. R. (2010). Image inpainting based on local optimization, International Conference on Patteren Recongnition (ICPR).
  14. Jiying Wu, Qiuqi Ruan. (May, 2008). A Novel Hybrid Image in-painting" IEEE Transactions on Image Processing.
  15. Komal Mahajan, Prof. Vaidya,B. M. (2012). Image in Painting Techniques: A survey, IOSR Journal of Computer Engineering ,5(4),pp:45-49.
  16. Bertalmio,M. , Bertozzi,A. L. and Sapiro,G. (Dec 2001). Navier stokes, Fluid Dynamics, and Image and Video Inpainting, Proceedings of Conf. Comp. Vision Pattern Rec. , Hawai, pp:355–362.
  17. Bertalmio,M. , Sapiro,G. , Caselles,V. , and Ballester,C. (July 2000). Image Inpainting, Proceedings of SIGGRAPH, New Orleans, USA, pp: 417-424.
  18. Bertalmio,M. , Vese,L. , Sapiro,G. , and Osher,S. (2003). Simultaneous Structure and Texture Image Inpainting, Proceedings of IEEE conference on Computer Vision and Pattern Recognition.
  19. Oliveira, M. , Bowen, B. , McKenna, R. , Chang,Y. (2001) . Fast Digital Inpainting, Proceedings of the International Conference on Visualization, Imaging and Image Processing, Marbella, Spain, pp: 261–266.
  20. Mahalingam, Vijay Venkatesh. (2010). Digital Inpainting Algorithms and Evaluation, University of Kentucky,Doctoral Dissertations. Paper 55, http://uknowledge. uky. edu/gradschool_diss/55
  21. Michael E Taschler. (2006). A Comparative Analysis of Image Inpainting Techniques,The University of York, pp:01-120.
  22. Mohiy M. Hadhoud, Kamel. A. Moustafa and Sameh. Z. Shenoda. ( 2006). Digital Images Inpainting using Modified Convolution Based Method, International Journal of Signal Processing, Image Processing and Pattern Recognition.
  23. Neelima1, N. , Arulvan,M. . (2013). Object Removal by Region Based Filling Inpainting, IEEE ,978-1-4673-5301-4.
  24. Nirali Pandya and Bhailal Limbasiya. (December 2013). A Survey on Image Inpainting Techniques , International Journal of Current Engineering and Technology, 3(5),pp: 1828-1831.
  25. Gonzales,R. C. , and Woods,R. E. (2002). Digital Image Processing, Second Edition, Prentice Hall, ISBN: 0-201-18075-8.
  26. Cham,T. , and Shen,J. (2001). Local inpainting models and TV inpainting, SIAM Journal on Applied Mathematics,62,pp:1019-1043
  27. Telea. (2004). An Image Inpainting Techniques Based On The Fast Maching Method,Journal Of Graphics Tools,9(1).
  28. Chan, T. F. , and Shen, J. ( September 2000). Non-Texture Inpainting by Curvature Driven Diffusion (CDD), UCLA Computational and Applied Mathematics ,pp:00-35.
  29. Chan,T. F. , Shen, J. ,and Vese, L. ( Dec. 2002). Variational PDE Models in Image Processing, UCLA Computational and Applied Mathematics , pp:02-61.
  30. Yasmine Nader El-Glaly. (2007). A Thesis on Development of PDE-based Digital Inpainting Algorithm Applied to Missing Data in Digital Images.
  31. Zhongyu Xu, Xiaoli Lian and Lili Feng. (2008). Image Inpainting Algorithm Based on Partial Differential Equation, IEEE Computer Society,International Colloquium on Computing, Communication, Control, and Management,ISSN:978-0-7695-3290-5.
Index Terms

Computer Science
Information Sciences

Keywords

Inpainting Texture Structure Image Occlusion Object Removal Algorithm Exemplar

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