Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Performance Analysis of Image Denoising Technique using Neural Network

Print
PDF
International Journal of Computer Applications
© 2015 by IJCA Journal
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 and Rohini Saxena. Article: Performance Analysis of Image Denoising Technique using Neural Network. International Journal of Computer Applications 122(13):36-39, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Shrish Pathak and Mukesh Kumar and A.k.jaiswal and Rohini Saxena},
	title = {Article: Performance Analysis of Image Denoising Technique using Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {122},
	number = {13},
	pages = {36-39},
	month = {July},
	note = {Full text available}
}

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

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