American Journal of Software Engineering and Applications

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An Overview of Restoration Algorithms for Digital Images

Received: 14 September 2016    Accepted: 23 September 2016    Published: 21 August 2017
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Abstract

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.

DOI 10.11648/j.ajsea.s.2016050301.17
Published in American Journal of Software Engineering and Applications (Volume 5, Issue 3-1, May 2016)

This article belongs to the Special Issue Advances in Computer Science and Information Technology in Developing Countries

Page(s) 30-33
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Image Restoration, Object Removal, Scattering-Based Restoration

References
[1] Christine, Guillemot; Olivier, Le Meur; “Image Inpainting Overview and recent advances”, IEEE signal processing magazine, pp. 127-144, january. 2014.
[2] 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.
[3] M. Elad, Sparse and Redundant Representations: From Theory to Applicationsin Signal and Image Processing. New York: Springer, 2010.
[4] 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.
[5] 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.
[6] 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.
[7] A. Efros and T. Leung, “Texture synthesis by non-parametric sampling,” in Proc. Int. Conf. Computer Vision (ICCV), sept. 1999, pp. 1033-1038.
[8] 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.
[9] 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.
[10] 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.
Author Information
  • Department of Computer Engineering, Khorasgan (Isfahan) Branch, Islamic Azad University, Isfahan, Iran

  • Department of Computer Engineering, Tehran South Branch, Islamic Azad University, Tehran, Iran

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  • APA Style

    Arman Nejahi, Aydin Parsa. (2017). An Overview of Restoration Algorithms for Digital Images. American Journal of Software Engineering and Applications, 5(3-1), 30-33. https://doi.org/10.11648/j.ajsea.s.2016050301.17

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    ACS Style

    Arman Nejahi; Aydin Parsa. An Overview of Restoration Algorithms for Digital Images. Am. J. Softw. Eng. Appl. 2017, 5(3-1), 30-33. doi: 10.11648/j.ajsea.s.2016050301.17

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    AMA Style

    Arman Nejahi, Aydin Parsa. An Overview of Restoration Algorithms for Digital Images. Am J Softw Eng Appl. 2017;5(3-1):30-33. doi: 10.11648/j.ajsea.s.2016050301.17

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  • @article{10.11648/j.ajsea.s.2016050301.17,
      author = {Arman Nejahi and Aydin Parsa},
      title = {An Overview of Restoration Algorithms for Digital Images},
      journal = {American Journal of Software Engineering and Applications},
      volume = {5},
      number = {3-1},
      pages = {30-33},
      doi = {10.11648/j.ajsea.s.2016050301.17},
      url = {https://doi.org/10.11648/j.ajsea.s.2016050301.17},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajsea.s.2016050301.17},
      abstract = {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.},
     year = {2017}
    }
    

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    T1  - An Overview of Restoration Algorithms for Digital Images
    AU  - Arman Nejahi
    AU  - Aydin Parsa
    Y1  - 2017/08/21
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    JF  - American Journal of Software Engineering and Applications
    JO  - American Journal of Software Engineering and Applications
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    AB  - 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.
    VL  - 5
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    ER  - 

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