An Overview of Restoration Algorithms for Digital Images
American Journal of Software Engineering and Applications
Volume 5, Issue 3-1, May 2016, Pages: 30-33
Received: Sep. 14, 2016;
Accepted: Sep. 23, 2016;
Published: Aug. 21, 2017
Views 1733 Downloads 60
Arman Nejahi, Department of Computer Engineering, Khorasgan (Isfahan) Branch, Islamic Azad University, Isfahan, Iran
Aydin Parsa, Department of Computer Engineering, Tehran South Branch, Islamic Azad University, Tehran, Iran
Follow on us
Image restoration refers to the process of restoration of lost or corrupted data in the image. In recent years, numerous methods with different functions in the reconstruction of noisy images or text replacement, hiding waste in the context of transferring of corrupted image, object removal in the context of editing, or removing the image prohibition on the transfer of image-based perspectives are presented which are distinct from the photos taken by the cameras. This article attempts to investigate the most appropriate and satisfactory method among different algorithms of image restoration. Scattered frequencies are considered to remove restoration problem with the emergence of sporadic cases and intensive observations. Scattering-based techniques are more suitable for filling large context areas. The algorithm is based on the assumption that the image (or patch) on a specified basis, spread (i.e., discrete cosine transform (DCT) or shock waves) with the goal that the restored image to be physically acceptable and satisfactory in appearance.
Image Restoration, Object Removal, Scattering-Based Restoration
To cite this article
An Overview of Restoration Algorithms for Digital Images, American Journal of Software Engineering and Applications. Special Issue: Advances in Computer Science and Information Technology in Developing Countries.
Vol. 5, No. 3-1,
2016, pp. 30-33.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Christine, Guillemot; Olivier, Le Meur; “Image Inpainting Overview and recent advances”, IEEE signal processing magazine, pp. 127-144, january. 2014.
S. Masnou and J. Morel, “level-lines based disocclusion,” in Proc. IEEE int. conf. Image Processing (ICIP), chicaho, IL. Oct. 1998, vol. 3, pp. 259-263.
M. Elad, Sparse and Redundant Representations: From Theory to Applicationsin Signal and Image Processing. New York: Springer, 2010.
O. Guleryuz, “Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-Part II: Adaptive algorithms,” IEEE Trans. Image Processing, vol. 15, no. 3, pp. 555–571, Mar. 2006.
X. Li, “Image recovery via hybrid sparse representations: A deterministic annealing approach,” IEEE J. Select. Topics Signal Process, vol. 5, no. 5, pp. 953–962, Sept 2011.
M. Bertalmio, L. Vese, G. Sapiro, and S. Osher, “Simultaneous structure and texture image inpainting,” IEEE Trans. Image Processing, vol. 12, no. 8, pp. 882–889, Aug. 2003.
A. Efros and T. Leung, “Texture synthesis by non-parametric sampling,” in Proc. Int. Conf. Computer Vision (ICCV), sept. 1999, pp. 1033-1038.
Zhang W, Ru Y, Meng H, Liu M, Ma X, Wang L, Jiang B. A Precise-Mask-Based Method for Enhanced Image Inpainting. Mathematical Problems in Engineering. 2016 Feb 16; 2016.
Ciotta M, Androutsos D. Depth guided image completion for structure and texture synthesis. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2016 Mar 20 (pp. 1199-1203). IEEE.
Wang R, Tao D. Non-local auto-encoder with collaborative stabilization for image restoration. IEEE Transactions on Image Processing. 2016 May; 25(5): 2117-29.