CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Image Restoration Methods, Survey of Machine Learning Methods and an Over View

by G.S. Yogananda, Y.P. Gowramma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 31
Year of Publication: 2020
Authors: G.S. Yogananda, Y.P. Gowramma
10.5120/ijca2020920865

G.S. Yogananda, Y.P. Gowramma . Image Restoration Methods, Survey of Machine Learning Methods and an Over View. International Journal of Computer Applications. 175, 31 ( Nov 2020), 41-44. DOI=10.5120/ijca2020920865

@article{ 10.5120/ijca2020920865,
author = { G.S. Yogananda, Y.P. Gowramma },
title = { Image Restoration Methods, Survey of Machine Learning Methods and an Over View },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2020 },
volume = { 175 },
number = { 31 },
month = { Nov },
year = { 2020 },
issn = { 0975-8887 },
pages = { 41-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number31/31652-2020920865/ },
doi = { 10.5120/ijca2020920865 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:40:01.244591+05:30
%A G.S. Yogananda
%A Y.P. Gowramma
%T Image Restoration Methods, Survey of Machine Learning Methods and an Over View
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 31
%P 41-44
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image restoration is one of the crucial problems in image processing even it is the low level image processing activity. This research paper presents the overview of the image restoration, available standard noises, and sources of noises. Further it details the degradation model, available restoration techniques, medical image restoration and medical restoration techniques. The literature survey on machine learning in restoration is explored. Finally mentioned the research gap to carry out the further research.

References
  1. Giacomo Boracchi and Alessandro Foi , “Modeling the Performance of Image Restoration from Motion Blur”  IEEE Transactions on Image Processing , Volume: 21 , Issue: 8 , Aug. 2012 , Page(s): 3502 – 3517.
  2. Prabhishek Singh & Raj Shree, “A Comparative Study to Noise Models and Image Restoration Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 149 – No.1, September 2016.
  3. Anamika Maurya & Rajinder Tiwari, “A Novel Method of Image Restoration by using Different Types of Filtering Techniques”, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 3, Issue 4, July 2014.
  4. Shilpa Rani, Sonika Jindal, Bhavneet Kaur, “A Brief Review on Image Restoration Techniques”, International Journal of Computer Applications, Foundation of Computer Science (FCS), NY, USA, Volume 150 - Number 12, Year of Publication: 2016.
  5. P.Sumitra, “A Comparative study algorithm for Noisy Image Restoration in the field of Medical Imaging”, International Journal of Advanced Information Technology (IJAIT) Vol. 6, No. 1, February 2016.
  6. Ishfaq Bashir et al, “Image Restoration and the various Restoration Techniques used in the field of Digital Image ‘Processing”, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.6, June- 2017, pg. 390-393.
  7. Mohd. Junedul Haque & Churu, “A Brief Review of Image Restoration Techniques”, International Journal of Advanced Computing Research ISSN [2349-7130], Volume 01, 2014.
  8. Monika Maru, M. C. Parikh, “Image Restoration Techniques: A Survey”, International Journal of Computer Applications Foundation of Computer Science (FCS), NY, USA, Volume 160 - Number 6 Year of Publication: 2017.
  9. Christian J. Schuler et al, “A Machine Learning Approach for Non-blind Image Deconvolution”,  2013 IEEE Conference on Computer Vision and Pattern Recognition, 23-28 June 2013.
  10. Liyang Wei ; Yongyi Yang ; R.M. Nishikawa ; Yulei Jiang, “A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications”,  IEEE Transactions on Medical Imaging, Volume: 24 , Issue: 3 , March 2005.
  11. Ying Shen and Weihua Zhu, “Medical Image Processing using A Machine Vision-based Approach”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 6, No. 3, June, 2013.
  12. Ruohan Gao &  Kristen Grauman, “On-Demand Learning for Deep Image Restoration”, Published in IEEE International Conference on Computer Vision…2017
  13. Komal Sharma et al, “Brain Tumor Detection based on Machine Learning Algorithms”, International Journal of Computer Applications (0975 – 8887) Volume 103 – No.1, October 2014.
  14. Bradley J. Erickson et al, “Machine Learning for Medical Imaging”, Radiographics, vol 27, No 2, feb 17 2017.
  15. Nalin kumar et al, “Noise Removal and Filtering Techniques used in Medical Images”, Oriental Journal of Computer Science & Technology, March 2017, Vol. 10, No. (1), Pgs. 103-113.
Index Terms

Computer Science
Information Sciences

Keywords

Restoration Noise Machine Learning

Powered by PhDFocusTM