CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Performance Analysis of Image Denoising Technique using Neural Network

by Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 13
Year of Publication: 2015
Authors: Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena
10.5120/21764-5013

Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena . Performance Analysis of Image Denoising Technique using Neural Network. International Journal of Computer Applications. 122, 13 ( July 2015), 36-39. DOI=10.5120/21764-5013

@article{ 10.5120/21764-5013,
author = { Shrish Pathak, Mukesh Kumar, A.k.jaiswal, Rohini Saxena },
title = { Performance Analysis of Image Denoising Technique using Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 13 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number13/21764-5013/ },
doi = { 10.5120/21764-5013 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:29.832450+05:30
%A Shrish Pathak
%A Mukesh Kumar
%A A.k.jaiswal
%A Rohini Saxena
%T Performance Analysis of Image Denoising Technique using Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 13
%P 36-39
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image processing is widely applied in various area of applications such as Medical, military, agriculture, etc. . The problem which generally occurs in image processing is the removal of noise generated due to various sources. In this paper a new approach based on neural network technique is proposed for the removal of noise. This technique follows three levels. This technique combines the advantages of filtering, neural network and bayes shrinkage technique. The noisy image is first passed through a bilateral filter and neural network is applied to the filtered image and the output of NN is then applied to bayes shrink. The proposed method outperforms other methods both visually and in case of objective quality peak-signal-to-noise ratio (PSNR) and MSE. Proposed method is verified for additive white Gaussian noise.

References
  1. I. Daubechies, "Ten Lectures on Wavelets". Philadelphia, PA: SIAM,
  2. D. L. Donoho and I. M. Johnston, (1994) "Ideal spatial adaptation by wavelet shrinkage".
  3. D. L. Donoho, I. M. Johnston, G. Kerkyacharian, and D. Picard, (1995) "Wavelet shrinkage: Asymptopia?".
  4. S. G. Chang, B. Yu and M. Vetterli, (2000) "Adaptive wavelet thresholding for image denoising and compression".
  5. R. C. Gonzalez and R. E. Woods, (2002) "Digital Image Processing".
  6. M. Vetterli and C. Herley, (1992) "Wavelet and filter banks: Theory and design".
  7. M. K. Mihcak, I. Kozintsev and K. Ramchandran, (1999) "Spatially Adaptive Statistical Modeling of Wavelet Image Coefficients and its Application to Denoising".
  8. Ming Zhang and Bahadir K. Gunturk( 2008)"Multiresolution Bilateral Filtering for Image Denoising,".
  9. D. L. Donoho, (1995)"Denoising and soft thresholding,".
  10. P. Bao and L. Zhang, (2003). "Noise reduction for magnetic resonance images via adaptive multiscale products thresholding,".
  11. A. Pizurica, W. Philips, I. Lemahieu, and M. Acheroy, (2003) "A versatile wavelet domain noise filtration technique for medical imaging,".
  12. L. Sendur and I. W. Selesnick, ( 2002)"Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency,".
  13. Q. Pan et al. , (1999) "Two denoising methods by wavelet transform,".
  14. S. M. Smith and J. M. Brady, (1997) "Susan—A new approach to low level image processing,".
  15. A. Macovski, (1996) "Noise in MRI".
  16. F. Luisier, T. Blu, and M. Unser, (2007) "A new sure approach to image denoising: Inter-scale orthonormal wavelet thresholding,".
  17. Junyuan Xie, Linli Xu and Enhong Chen(2012) "Image Denoising and Inpainting with Deep Neural Networks".
  18. G. Hinton, S. Osindero, and Y. Teh. (2006) "A fast learning algorithm for deep belief nets".
  19. K. Hornik, M. Stinchcombe, and H. White. (1989) "Multilayer feed forward networks are universal approximators"
Index Terms

Computer Science
Information Sciences

Keywords

Image denoising wavelet threshoding Bayes threshold Multiscale product threshold PSNR MSE DWT IDWT

Powered by PhDFocusTM