Image Restoration of Damaged Mural images based on Image Decomposition
M. Rathika1 , R. Shenbagavalli2
Section:Research Paper, Product Type: Journal Paper
Volume-07 ,
Issue-08 , Page no. 32-37, Apr-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7si8.3237
Online published on Apr 10, 2019
Copyright © M. Rathika, R. Shenbagavalli . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: M. Rathika, R. Shenbagavalli, “Image Restoration of Damaged Mural images based on Image Decomposition,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.08, pp.32-37, 2019.
MLA Style Citation: M. Rathika, R. Shenbagavalli "Image Restoration of Damaged Mural images based on Image Decomposition." International Journal of Computer Sciences and Engineering 07.08 (2019): 32-37.
APA Style Citation: M. Rathika, R. Shenbagavalli, (2019). Image Restoration of Damaged Mural images based on Image Decomposition. International Journal of Computer Sciences and Engineering, 07(08), 32-37.
BibTex Style Citation:
@article{Rathika_2019,
author = {M. Rathika, R. Shenbagavalli},
title = {Image Restoration of Damaged Mural images based on Image Decomposition},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {07},
Issue = {08},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {32-37},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=911},
doi = {https://doi.org/10.26438/ijcse/v7i8.3237}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i8.3237}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=911
TI - Image Restoration of Damaged Mural images based on Image Decomposition
T2 - International Journal of Computer Sciences and Engineering
AU - M. Rathika, R. Shenbagavalli
PY - 2019
DA - 2019/04/10
PB - IJCSE, Indore, INDIA
SP - 32-37
IS - 08
VL - 07
SN - 2347-2693
ER -
Abstract
The most significant challenges in image processing and pattern recognition is image decomposition and restoration. Image Restoration is the operation of taking a noisy image and estimating the clean, original image. Corrupt image may come in many forms such as motion blur, noise and camera mis-focus. Restoration is a process of eliminating degraded noise and increases the quality of image. Image decomposition is to decompose an image into its component structures. When two image signals are considered, a combined image signal should contain the image structure of both these signals In this paper, the mural images are decomposed into cartoon component or geometrical part of blurred images and Texture component or small scale special pattern using Bilateral filter. In cartoon component augmented Lagrangian method has been used to fill the missing pixel. In texture component the blurring can be removed using median filter and conservative filter. Median filter is used to remove noise and conservative filter is used for smoothening the image. By using these filters, degraded mages can be restored successfully. The restoration efficiency can be measured with MSE (Mean Square error) and PSNR (Peak Signal to Noise Ratio) parameter. Various mural images have been analyzed and tested. The accuracy is comparatively better than the existing.
Key-Words / Index Term
Image restoration, Cartoon component, Texture component, Image decomposition
References
[1] A.Deepika, K.Raja Sundari, R.Ravi,”Image Decomposition and Restoration for Blurred Images Using Filtering Techniques”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3 Issue 3, March 2014
[2] Michal K. Ng, Xiaoming and Wenxing Zhang, ”Coupled Variational Image Decomposition and restoration Model for Blurred Cartoon-Plus-Texture Images With Missing Pixels”, IEEE Trans. Image Processing, vol. 22, No. 6, pp. 2233-2246,2013.
[3]. G. Karuna “Image Decomposition and Restoration Model for BlurredCartoon and Texture Images with Filtering Techniques” International Journal of Electronics, Electrical and Computational SystemIJEECSISSN 2348-117XVolume 6, Issue 11 November 2017
[4] www.google.com “mural images”, “Mean Square error” , “Peak Signal to Noise Ratio”,”median filter”,“conservative filter”,”Lagrangian method”
[5] M. Bertalmio, G. Sapiro, V. Caselles, and C. Ball ester, Image Inpainting,in Proc. 27th Annu. Conf. Comput. Graph. Interact. Tech., 2000,pp. 417–424.
[6] M. Bertalmio, L. Vese, G. Sapiro, and S. Osher, Simultaneous structure and texture image Inpainting, IEEE Trans. Image Process., vol. 12, no.8, pp. 882–889, Aug. 2003.
[7] J.-F. Cai, R. H. Chan, and Z. Shen, Simultaneous cartoon and textureInpainting, Inverse Prob. Imaging, vol. 4, no. 3, pp. 379–395