We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

An Extensive Estimation and Analysis of Image Denoising

by Prerna Rajput, Shiv Kumar Singh Tomar, Bhupesh Gour
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 8
Year of Publication: 2015
Authors: Prerna Rajput, Shiv Kumar Singh Tomar, Bhupesh Gour
10.5120/19850-1719

Prerna Rajput, Shiv Kumar Singh Tomar, Bhupesh Gour . An Extensive Estimation and Analysis of Image Denoising. International Journal of Computer Applications. 113, 8 ( March 2015), 36-41. DOI=10.5120/19850-1719

@article{ 10.5120/19850-1719,
author = { Prerna Rajput, Shiv Kumar Singh Tomar, Bhupesh Gour },
title = { An Extensive Estimation and Analysis of Image Denoising },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 8 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number8/19850-1719/ },
doi = { 10.5120/19850-1719 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:50:28.049011+05:30
%A Prerna Rajput
%A Shiv Kumar Singh Tomar
%A Bhupesh Gour
%T An Extensive Estimation and Analysis of Image Denoising
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 8
%P 36-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image denoising is the technique of removal of the noise from the image contaminated by additive Gaussian noise without loss of features of image. It is a fundamental process in pattern recognition and image processing. When an Image is captured many factors such as lighting spectra, source, intensity and camera Characteristics affect the image. The main factor that reduces the quality of the image is Noise. It hides the important information of images and changes value of image pixels at key locations causing blurring and various other deformities. Noises must be removed from the images without loss of any information with it. Noise removal is the preprocessing stage of image processing. There are many types of noises which may corrupt the images. These noises are appear on images in many ways: at the time of acquisition due to noisy sensors, due to defective scanner or due to faulty digital camera device, as a result of transmission channel errors, due to corrupted storage media. There are numerous researches have been done on wavelet based denoising for estimation of parameters such as variance of the multi scale Linear minimum mean square error. In this review paper we have presented an extensive analysis and literature review on image denoising.

References
  1. Leifu Gao; Chao Li, "An adaptive TV model for image denoising," Natural Computation (ICNC), 2013 Ninth International Conference on , vol. , no. , pp. 766,770, 23-25 July 2013.
  2. Yao Zhao; Jianguo Liu; Bingchen Zhang; Wen Hong; Yirong Wu, "An adaptive total variation regularization method for SAR image despeckling," Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International , vol. , no. , pp. 3084,3087, 21-26 July 2013.
  3. Boyat, A. ; Joshi, B. K. , "Image denoising using wavelet transform and median filtering," Engineering (NUiCONE), 2013 Nirma University International Conference on , vol. , no. , pp. 1,6, 28-30 Nov. 2013.
  4. Huijie Guo; Weihai Fang; Xin Wen; Feng Nian, "Image enhancement based on matrix completion," Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSQRWC), 2013 , vol. , no. , pp. 357,360, 21-25 July 2013.
  5. Veeramani, T. ; Rajagopalan, A. N. ; Seetharaman, G. , "Restoration of foggy and motion-blurred road scenes," Image Processing (ICIP), 2013 20th IEEE International Conference on , vol. , no. , pp. 928,932, 15-18 Sept. 2013.
  6. Gibson, K. B. ; Nguyen, T. Q. , "Fast single image fog removal using the adaptive Wiener filter," Image Processing (ICIP), 2013 20th IEEE International Conference on , vol. , no. , pp. 714,718, 15-18 Sept. 2013.
  7. Ota, A. ; Yoshida, T. ; Ikehara, M. , "Blocking artifacts reduction of DCT compressed image based on block wiener filtering," Advanced Technologies for Communications (ATC), 2013 International Conference on , vol. , no. , pp. 85,89, 16-18 Oct. 2013.
  8. Coifman, Ronald R. , and David L. Donoho. Translation-invariant de-noising. Springer New York, 1995.
  9. Chang, S. Grace, Bin Yu, and Martin Vetterli. "Adaptive wavelet thresholding for image denoising and compression. " Image Processing, IEEE Transactions on 9. 9 (2000): 1532-1546.
  10. Om, Hari, and Mantosh Biswas. "A generalized image denoising method using neighbouring wavelet coefficients. " Signal, Image and Video Processing (2013): 1-10.
  11. Sendur, Levent, and IvanW. Selesnick. "Bivariate shrinkage with local variance estimation. " Signal Processing Letters, IEEE 9. 12 (2002): 438-441.
  12. Pizurica, Aleksandra, et al. "A joint inter-and intrascale statistical model for Bayesian wavelet based image denoising. " Image Processing, IEEE Transactions on 11. 5 (2002): 545-557.
  13. Hou, Zujun. "Adaptive singular value decomposition in wavelet domain for image denoising. " Pattern Recognition 36. 8 (2003): 1747-1763.
  14. Boubchir, Larbi, and Boualem Boashash. "Wavelet denoising based on the MAP estimation using the BKF prior with application to images and EEG signals. " (2013): 1-1.
  15. Zhang, Guo-Duo, et al. "Image denoising based on support vector machine. " Engineering and Technology (S-CET), 2012 Spring Congress on. IEEE, 2012.
  16. Chen, G. Y. , Tien D. Bui, and Adam Krzyzak. "Image denoising using neighbouring wavelet coefficients. " Integrated Computer-Aided Engineering 12. 1 (2005): 99-107.
  17. Chen, G. Y. , and T. D. Bui. "Multiwavelets denoising using neighboring coefficients. " Signal Processing Letters, IEEE 10. 7 (2003): 211-214.
  18. Ruikar, Sachin, and D. D. Doye. "Image denoising using wavelet transform. "Mechanical and Electrical Technology (ICMET), 2010 2nd International Conference on (pp 509-515). IEEE, 2010.
  19. Jain AK. Fundamental of digital image processing. Prentice Hall;Upper Saddle River,NJ;2001.
  20. Liua, Jia, Caicheng Shi, and Meiguo Gao. "Image denoising based on BEMD and PDE. " Computer Research and Development (ICCRD), 2011 3rd International Conference on. Vol. 3. IEEE, 2011.
  21. Om, Hari, and Mantosh Biswas. "MMSE based map estimation for image denoising. " Optics Laser Technology 57 (2014): 252-264.
  22. Jiang, Jun, et al. "An improved adaptive wavelet denoising method based on neighboring coefficients. " Intelligent Control and Automation (WCICA), 2010 8th World Congress on. IEEE, 2010.
  23. Donoho, David L. "Unconditional bases are optimal bases for data compression and for statistical estimation. " Applied and computational harmonic analysis 1. 1 (1993): 100-115.
  24. Mallat, Stephane G. "A theory for multiresolution signal decomposition: the wavelet representation. " Pattern Analysis and Machine Intelligence, IEEE Transactions on 11. 7 (1989): 674-693.
  25. Abramovich, Felix, Theofanis Sapatinas, and BernardW. Silverman. "Wavelet thresholding via a Bayesian approach. " Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60. 4 (1998): 725-749
Index Terms

Computer Science
Information Sciences

Keywords

Optimization Image denoising Image restoration & TV model

Powered by PhDFocusTM