Convolutional Neural Network Based Image Denoising for Better Quality of Images

  • Abstract
  • Keywords
  • References
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  • Abstract

    This work gives a survey by comparing the different methods of image denoising with the help of wavelet transforms and Convolutional Neural Network. To get the better method for Image denoising, there is distinctive merging which have been used. The vital role of communication is transmitting visual information in the appearance of digital images, but on the receiver side we will get the image with corruption. Therefore, in practical analysis and facts, the powerful image denoising approach is still a legitimate undertaking. The algorithms which are very beneficial for processing the signal like compression of image and denoising the image is Wavelet transforms. To get a better quality image as output, denoising methods includes the maneuver of data of that image. The primary aim is wavelet coefficient modification inside the new basis, by that the noise within the image data can be eliminated. In this paper, we suggested different methods of image denoising from the corrupted images with the help of different noises like Gaussian and speckle noises. This paper implemented by using adaptive wavelet threshold( Sure Shrink, Block Shrink, Neigh Shrink and  Bivariate Shrink) and Convolutional Neural Network(CNN) Model, the experimental consequences the comparative accuracy of our proposed work.



  • Keywords

    Wavelet thresholding, sure shrink, block shrink, neigh shrink, bivariate shrink, CNN model.

  • References

      [1] Anand BG & Kiran KG “Image Denoising Using Contourlet Transform with Total Variation and Nonlocal Similarity Model”, International Journal of Pure and Applied Mathematics, Vol.118, (2018), pp.1429-1441.

      [2] Zhing G & Xiaohai Y, “Theory and application of MATLAB Wavelet analysis tools”, National defense industry publisher, Beijing, (2004), pp.108-116.

      [3] Gyaourova A, Kamath C & Fodor IK, Undecimated wavelet transforms for image de-noising (No. UCRL-ID-150931). Lawrence Livermore National Lab., CA (US), (2002).

      [4] Burrus CS, Gopinath RA, Guo H, Odegard JE & Selesnick IW, Introduction to wavelets and wavelet transforms: a primer, New Jersey: Prentice hall, (1998).

      [5] Abramovich F & Benjamini Y, “Adaptive thresholding of wavelet coefficients”, Computational Statistics & Data Analysis, Vol.22, No.4, (1996), pp.351-361.

      [6] Abramovich F, Sapatinas T & Silverman BW, “Wavelet thresholding via a Bayesian approach”, Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol.60, No.4, (1998), pp.725-749.

      [7] Cai Z, Cheng TH, Lu C & Subramanian KR, “Efficient wavelet-based image denoising algorithm”, Electronics Letters, Vol.37, No.11, (2001), pp.683-685.

      [8] Donoho DL & Johnstone IM, “Denoising by soft thresholding”, IEEE Trans. on Inform. Theory, Vol.41, (1995), pp. 613-627.

      [9] Zhang XP & Desai MD, “Adaptive denoising based on SURE risk”, IEEE signal processing letters, Vol.5, No.10, (1998), pp.265-267.

      [10] Chang SG, Yu B & Vetterli M, “Adaptive wavelet thresholding for image denoising and compression”, IEEE transactions on image processing, Vol.9, No.9, (2000), pp.1532-1546.

      [11] Chen GY, Bui TD & Krzyzak A, “Image denoising using neighbouring wavelet coefficients”, Integrated Computer-Aided Engineering, Vol.12, No.1, (2005), pp.99-107.

      [12] Donoho DL & Johnstone IM, “Adapting to unknown smoothness via wavelet shrinkage”, Journal of the american statistical association, Vol.90, No.432, pp.1200-1224.

      [13] Stein CM, “Estimation of the mean of a multivariate normal distribution”, The annals of Statistics, (1981), pp.1135-1151.

      [14] Cai TT, “Adaptive wavelet estimation: a block thresholding and oracle inequality approach”, Annals of statistics, (1999), pp.898-924.

      [15] Chen GY, Bui TD & Krzyżak A, “Image denoising with neighbour dependency and customized wavelet and threshold”, Pattern recognition, Vol.38, No.1, (2005), pp.115-124.

      [16] Cai TT & Zhou HH, “A data-driven block thresholding approach to wavelet estimation”, The Annals of Statistics, Vol.37, No.2, (2009), pp.569-595.

      [17] Wink AM & Roerdink JB, “Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing”, IEEE transactions on medical imaging, Vol.23, No.3, (2004), pp.374-387.

      [18] Yoon BJ & Vaidyanathan PP, “Wavelet-based denoising by customized thresholding”, IEEE International Conference on Acoustics, Speech, and Signal Processing, (2004), pp.2-925.

      [19] Pizurica A, Wink AM, Vansteenkiste E, Philips W & Roerdink BJ, “A review of wavelet denoising in MRI and ultrasound brain imaging”, Current medical imaging reviews, Vol.2, No.2, (2006), pp.247-260.3

      [20] Akhpanov, S. Sabitov, R. Shaykhadenov (2018). Criminal pre-trial proceedings in the Republic of Kazakhstan: Trend of the institutional transformations. Opción, Año 33. 107-125.

      [21] D, Ibrayeva, Z Salkhanova, B Joldasbekova, Zh Bayanbayeva (2018). The specifics of the art autobiography genre. Opción, Año 33. 126-151.




Article ID: 17972
DOI: 10.14419/ijet.v7i3.27.17972

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