different techniques for the image denoising

1. Alisha P.B
3 rd Sem
M.Tech in
Wireless Technology
Internal Guide :
Prof .(Dr).GNANA SHEELA .K
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2. • Introduction
• Objective
• Goals Of Image Denoising
• Image Denoising Techniques
• How
• Why
• Block Diagram
• Tools
• Summary
• Reference
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3. • Noise models
* Additive Noise Model
* Multiplicative Noise Model
* salt & pepper noise
* Poisson noise
* Speckle Noise
• Types of noise
* Gaussian Noise
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GAUSSIAN NOISE
ORIGINAL
POISSON NOISE
SALT N PEPPER SPECKLE NOISE

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8. • Analysis of Image denoising techniques using
discrete wavelet transforms and find out the
efficient method with respect to type of the
image and noise in cooperate with it.
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9. Goals of Image Denoising
• To suppress the noise
• To preserve edges , image characteristics.
• To provide visual natural appearance
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10. Image Denoising Techniques
* Spatial Filtering
* Transform Domain Filtering
* Wavelet Based Thresholding Method
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* Linear filters
mean filter
wiener filter
* Non linear filters
median filter

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• Current pixel replaced by arithmetic
mean of it’s neighboring pixel values
Original input image Filtered output image

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w
• Comparing the received signal with the
estimation of a desired noise signal
Original input image Filtered output image

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• Centre value replaced by arithmetic
median of it’s neighboring pixel values
Original input image Filtered output image

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* Spatial frequency filtering
low pass filter & fast Fourier transform
* Wavelet domain filtering
wavelet transforms

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FREQUENCY
FILTERING
 Frequency Resolution
 Fast Fourier transform
 Transformation pixel by pixel

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WAVELET
FILTERING
 Time &Frequency Resolution
 Multiresolution
 Wavelet family

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* Non Adaptive threshold
number of data points
•Adaptive threshold
wavelet coefficient

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Non Adaptive
 VISU Shrink
Adaptive
 Sure Shrink
 Bayes Shrink
 Neigh Shrink
 Mod Neigh Shrink

20. why
• Sparsity
• Multiresolution Structure
• Multiscale Nature.
• Time and Frequency Localization
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21. How
• Discrete wavelet transform is adopted to decompose
the noisy image and get the wavelet coefficients.
• These wavelet coefficients are denoised with wavelet
threshold.
• Inverse transform is applied to the modified
coefficients and get denoised image
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22. Block Diagram
Block diagram of Image denoising using
Wavelet Transform
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Input image

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 Peak signal to noise ratio
 Mean square error
 Visual quality
 Structural similarity index
 Coefficient correlation

24. • MATLAB
• VHDL
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* Several well known algorithms for denoising
natural images were investigated and their
performances are comparatively assessed. The
results are simulated on MATLAB.2013a
• The proposed method gives significant
improvement in terms of image quality and
preserves the useful information

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[1] Rajni, Anutam, “Image Denoising Techniques –An
Overview,” International Journal of Computer, Vol. 86,
No.16, January 2015.
[2] P. Hedaoo and S. S. Godbole, “Wavelet Thresholding
Approach for Image Denoising,” International Journal of
Network Security & Its Applications, Vol. 3, No. 4, 2015.
[ 3] R. C. Gonzalez and R.E. Woods, Digital Image Processing.
2nd ed. Englewood Cliffs, NJ: Prentice-Hall; 2002 .
[4]Pizurica, A., Philips, W., Lemahieu, I., et al.: ‘A versatile
wavelet domain noise filtration technique for medical
imaging’, IEEE Trans.Med. Imaging, 2013
[5] Jean-Luc Starck, Emmanuel J. Candes, and David L.
Donoho. “The curvelet transform for image denoising,” IEEE
Transactions on image processing, vol. 11, no. 6, pp. 670-684,
2013.

27. [6] Kachouie N.N., Fieguth P. “A combined Bayesshrink
Wavelet-Ridgelet Technique for Image Denoising,” IEEE
international Conference onMultimedia and Expo, pp, 2015.
[7] Chang S.G., Bin Yu, Vitterli M. “Adaptive Wavelet
Thresholding for image Denoising and Compression,”IEEE
Transactions on Image Processing, vol. 9, Issue 9, pp.1532-
1546, 2014.
[8] Jiang Tao, Zhao Xin,DingWenwen,ChenJunqing. “Improved
ImageDenoising method based on Curvelet Transform,”
International Conference on Information and Automation, pp.
1086-1090, 2014.
[9] Donglei Li, ZheminDuan, MengJia, “New method based on
curvelet transform for image denoising,” IEEE International
Conference on Measuring Technology and Mechatronics
Automation, vol. 2, pp. 760-763, 2014
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28. [10] QianzongBao, Qingchun Li, “Translation invariant denoising
using neighbouring curvelet coefficients,” 3rdInternational
Workshop on Intelligent Systems and Applications , pp. 1-4,
2013.
[11] RoopaliGoel, Ritesh Jain. Speech signal noise reduction by
wavelets, vol-2march 2013[12] Mohammed bahoura, Jean
rouat .Wavelet noise reduction:application to speech
enhancement.
[13] Rajeev aggarwal, Jay singh , Vijay gupta, Dr.
Anubhutikhare. Elimination of white noise from speech signal
using wavelet transform by soft and hard threoiling,
IJEECE,vol.1(2), 2011.
[14] YANG Dali, XU mingxing, Wu wenhu , ZHENG fang. A
noise cancellation method based on wavelet transform,oct 13-
15,2014
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