IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 102-110

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

A New Approach for detection and reducti reduction of noise in degraded image Miss. Gayatri P. Bhelke1, Prof. V. S.Gulhane2,, Prof. N. D. Shelokar3 1

M. E student , Department of Computer Science and Engineering, Sipna College of Engineering And Technology, Amravati Amravati, Maharashtra, India [email protected]

2

Associate Professor, Department of Computer Science and Engineering, Sipna College of Engineering And Technology, Amravati Amravati, Maharashtra, India [email protected]

3

Assistant Professor, Department of Computer Science and Engineering, Sipna College of Engineering And Technology, Amravati Amravati, Maharashtra, India [email protected]

Abstract The usually images contains different kinds of noises in process of receiving, coding and transmission. This paper studies the problem of image restoration of images corrupted by noise. In this paper we propose an efficient method, which can detect and reduce noise from corrupted images. The algorithm consists of two steps: noise detection and noise cancellation. Noise detection is used to detect the type of noise present in image such as salt and pepper noise ,Gaussian noise etc. where as in noise cancellation stage it try to reduce the noise by using efficient filtering technique.

Keywords: Image Restoration, Degraded Images, Denoising, Noise Detection, Salt and pepper Noise, Gaussian, Image Noise

1. Introduction Noise is unwanted information which is present in the images, which affect the quality of images. Noise can be unavoidable in communication networks, and its presence can have terrible effects upon the data being sent [3]. Image Processing is a technique that improves the quality of raw Images capture in normal day-to-day life for many applications. Images captured by digital cameras could be affected by noise due to random variations of pixel elements in the camera sensors. There are various types of image noise that can present in the image such as Gaussian noise, salt and pepper noise, random valued impulse noise, speckle noise, Uniform noise [8].

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1.1 Salt and Pepper Noise Salt and Pepper noise is also known as Impulse Noise. This noise can be caused by sharp & sudden disturbances in the image signal. It represents itself is randomly occurring white or black (or both) dots over image. 1.2 Gaussian Noise Gaussian Noise is caused by random fluctuations in the signal. Its modeled by random values added to an image 1.3 Speckle Noise Speckle noise can be modeled by random values multiplied by pixel values of an image.

1.4 Uniform Noise Uniform noise is also known as quantization noise. It is caused by quantizing the pixels of a sensed image to a number of discrete levels. It has an approximately uniform distribution In this work, we will present a new, faster and more efficient noise detection and reduction method for degraded images. The algorithm which is used to detect the presence of noise and to remove it, should be theoretically, and computationally as simple as possible. A well-defined process of detecting and reducing certain types of noise in transmitted images would have to be a somewhat crude for speed. The main aim of noise reduction is to smother the noise, and also probably to safeguard the sharpness of edge and feature information [ 60]. Here, we have discussed a new efficient noise detection algorithm that will be able to allow the receiver to know what type of filtering method should be applied for the type of noise detected in given image.

2. Related work Many of the current papers dealing with noise in communication networks which propose a two-stage method of impulse noise reduction where in the first stage noise is detected and in the second it compensated for a filtering technique. As per Ming Yan’s paper [26] Adaptive center-weighted median filter (ACWMF) is appropriate method for removing random-valued impulse noise, when the noise level is not high. Paper presents a general algorithm for blind image inpainting and removing impulse noise by iteratively restoring the image and identifying the damaged pixels. Qin Zhiyuan et.al ‘s[39] describe A Robust Adaptive Image Smoothing Algorithm in which analysis of some smoothing algorithms are given which include Edge Preserved filtering ,Adaptive medium filter, Robust smoothing filter and Gradient weighting filter. Robust adaptive algorithm combines multiwindow templates, gradient weighting, constant gray output on non-pulse pixel and the improved adaptive smoothing algorithm. In Addition to Smoothing algorithm, paper introduces the methods of enlarging windows and selecting sub template windows to remove salt and pepper noise with large space intensity. Because it uses a new algorithm by combining nonlinear filtering and linear filtering according to their respective adaptation to different noises. In Deborah D. Duran-Herrmann et.al [3] paper two stage process is given in which first stage detect the type of noise and in second stage which type of filter is suitable for detected noise is given to eliminate the noise. “Noise removal Algorithm for Image Corrupted by Additive Gaussian Noise”, [49] describe two fundamental mathematical morphological operations , that are dilation and erosion. Dilation adds pixels to the boundaries of objects in an image, where as erosion removes pixels on object boundaries. Mathematical morphological operations are also useful in smoothing and sharpening. Paper presents noise removal algorithm for gray scale images corrupted by Gaussian noise. “The Effect of shape The Effect of Shape and Weight Towards the Performance of Simple Adaptive Median Filter in Reducing Impulse Noise Level from Digital Images”,[29] compare simple Miss. Gayatri P. Bhelke,

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adaptive median filter with three new filters that are Circular SAM (CSAM), Weighted SAM (WSAM), and Weighted CSAM (WCSAM). “A Noise Fading Technique for Images Highly Corrupted with Impulse Noise”, [15] describes a method which is used to remove the impulse noise from highly corrupted image with impulse noise. Method introduces a spike detection technique (SDT) and pixel restoring median filter (PRMF) for denoising the degraded images. The SDT is used for discriminating between corrupted and uncorrupted image pixels. The corrupted pixels are restored using PRMF technique. Given iterative denoising technique is repeated until the corrupted pixels in the recovered image reduce to zero.

3. Proposed Work Our given algorithm uses a two-stage process of determining three things which are given below, • • •

Presence of noise Type of noise such as impulse noise or Gaussian The effective filtering method for removing noise

Proposed technique consist of two stage that are given below, 1. 2.

Noise Detection Noise cancellation

Noise detection is used to detect the type of noise present in the image where as noise cancellation stage is used to reduce noise from image. Working of both stages are explain in following section

3.1 Noise Detection Noise detection is first stage of our given algorithm. The detection technique is applied to the corrupted image to determine which type of noise is present in corrupted image. For detecting noise it first calculate the image histogram the according the histogram it calculated the third order histogram ratio and intensity ratio. According to the histogram and intensity ratio it determines the type of noise present in the image. Noise detection Algorithm: i.

Calculate Histogram of input image.

ii.

Sort histogram in descending order.

iii.

Calculate third order histogram ratio ,

iv.

Calculate intensity ratio

3.2 Noise Cancellation Second stage of our proposed technique is noise cancellation which is used to reduce the noise detected in the first stage by using efficient filtering technique. According to the type of noise detected in first stage, effective filtering technique is used for reducing noise present in the image. Following steps shows the working of adaptive median filter[29],

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i.

Find the value of K

ii.

Find the value of eta i.e approximation of the noise level in the image

iii.

Find the Rmin

iv.

Find the value w where w is square filter

v.

Based on the binary mask alpha, compute the number of "noise free pixels" contained in the contextual region of size Wx W, as defmed by the filter.

vi.

If the number of "noise free pixels" is less than eight pixels, increase the size of the square filter by two (i.e. W= W+2) and return to step 2.

vii.

Calculate the median value of pixels based on the "noise free pixels" contained in window of size Wx W.

3.

Experimental Result

If the noise gets detected in the noise detection stage of proposed technique then noise cancellation stage is performed for reducing noise from corrupted image. Performance of the technique is evaluated using PSNR value and Execution time. Higher the PSNR value, better the quality of image. Proposed technique is tested with different standard images such as Lena, Tulips, Mandrill etc. Following fig (2) shows the example of image corrupted by salt and pepper noise and reconstructed image by using effective filtering technique.

Fig.2 (a) Original Image, (b) Image corrupted by salt and pepper noise (c) Recovered image Following Fig (3) Shows experimental result for the Mandrill Image Fig3(a) shows the original Mandrill image Fig3(b) shows the 40% corrupted image by salt and pepper noise Fig3(c) shows Reconstructed image by using proposed technique.

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Fig.3 (a) Original Mandrill Image, (b) 40% salt and pepper noisy Image (c) Recovered image (Proposed technique). Following Table 1 compares the proposed technique with previously used techniques in terms of PSNR value and execution time for Mandrill image. Group of filter consist of FIDRM (filters for salt and pepper noise reduction), FIRE (fuzzy inference rule by else-action filter), PWLFIRE (piecewise liner FIRE), DSFIRE (dual step FIRE), FMF (Fuzzy median filter), [47]. PSNR Value(db) Filters FIRE FMF DSFIRE PWLFIRE FIDRM Proposed Technique

5% 24.3 27.7 30.3 32.6 34.4 80.74

25% 18.8 20.9 24.6 19.6 27.0 72.35

50% 12.8 14.4 16.6 12.2 23.2 67.07

Execution Time(seconds) 5% 225.0 59.5 660.1 62.1 4.9 0.75

25% 227.0 64.4 676.4 66.7 15.7 2.59

50% 208.8 55.4 611.0 57.8 39.1 4.22

Table 1 PSNR and Execution Time Result for Mandrill Image for Different noise levels and Different Filters

Following Fig. (4) Shows experimental result for the Lena Image Fig4.(a) shows the original Lena image Fig4.(b) shows the 40% corrupted image by salt and pepper noise Fig.4(c) shows Reconstructed image by using proposed technique.

Fig.4 (a) Original Lena Image, (b) 40% salt and pepper noisy Image (c) Recovered image(Proposed technique). Following Table 2 compares the proposed technique with previously used techniques in terms of PSNR value and execution time for Lena image. Group of filter consist of FIDRM (filters for salt and pepper noise reduction), FIRE (fuzzy inference rule by else-action filter), PWLFIRE (piecewise liner FIRE), DSFIRE (dual step FIRE), FMF (Fuzzy median filter), [47].

PSNR Value(db) Filters Miss. Gayatri P. Bhelke,

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Execution Time(seconds) 5%

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FIRE FMF DSFIRE PWLFIRE FIDRM Proposed Technique(AMF)

31.8 34.3 35.7 36.5 40.7 79.87

21.1 25.5 28.5 20.4 33.4 71.40

13.5 15.7 17.9 12.6 29.0 66.03

53.8 12.7 153.0 14.0 1.0 0.51

54.7 12.9 156.5 15.2 4.2 1.60

50.5 14.1 146.6 10.6 8.3 4.20

Table 2 PSNR and Execution Time Result For Lena Image for Different noise levels and Different Filters Following Fig (5) shows the example of image corrupted by Gaussian noise and restored image by using effective filtering technique .

Fig.5 (a) Original Image, (b) Image Corrupted by Gaussian Noise (c) Recovered image Following Fig (6) shows experimental result for the Tulips Image corrupted by Gaussian Noise.

Fig.6 (a) shows Original Image (b) image corrupted by Gaussian noise (c) Restored image after 1’st iteration (d) Restored image after 2’nd iteration (e) Restored image after 3’rd iteration (f) Restored image after 4’th iteration (g) Restored image after 5’th iteration (h) Restored image after 6’th iteration (i) Restored image after 7’th iteration. Miss. Gayatri P. Bhelke,

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Following Table3 compares the proposed technique for Lena image corrupted by Gaussian noise with previously used techniques in terms of PSNR value. Group of filter consist of Bilateral Filter (BF), the improved BF (Multiresolution Bilateral Filtering (MBF) (the best improvement for Bilateral Filter), Kernel regression for image denoising (LARK) and BLS-GSM [43]

PSNR Value(db) Noise variance Filters BF

1 --

MBF LARK BLS-GSM KSVD Proposed Technique

----63.3

2 --

5 --

10 31.29

15 29.13

20 27.46

--32.39 29.99 27.77 --32.41 30.29 28.63 --33.16 30.90 29.34 --33.66 31.48 29.95 62.82 62.30 61.91 61.86 61.64 Table 3 PSNR Result For Lena Image for Different noise Variance and Different Filters

References 1. 2. 3. 4.

5.

6.

7. 8. 9. 10. 11. 12. 13.

14. 15. 16. 17.

[B. Zhang et al 2008],B.K.Gunturk, “Multi resolution bilateral filtering for image denoising”, IEEETrans.ImageProcess.17(2008)2324–2333. [C.Tomasi et al1998],R.Manduchi, “Bilateral filtering for gray and colorimages”, International Conference on Computer Vision (ICCV),1998,pp.839–846. [Deborah D. Duran-Herrmann et al 2012], Qilin Qi, and Yaoqing (Lamar) Yang, “An Adaptive Algorithm for Corrupted Images” International Conference on Systems and Informatics (ICSAI 2012) [D. Ze-Feng et al 2007], Y. Zhou-Ping, and X. You-Lun “High Probability Impulse Noise- Removing Algorithm Based on Mathematical Morphology,” IEEE Signal Processing Letters, Vol. 14, No. 1, January 2007, pp. 31-34. [D.Atkins et al. 2011],K.Neshatian,M.Zhang, “ A domain in dependent genetic programming approach to automatic feature extraction for image classification”, IEEE Congresson Evolutionary Computation (CEC),Wellington,NewZealand,2011,pp.238–245. [D.Martin et al.2001],D.Fowlkes,J.Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics”, InternationalConferenceonComputerVision(ICCV), Vancouver,Canada,2011,pp.416–423. [Gouchol Pok et al 2003], Jyh-Chain Liu and A.S. Nair, “Selective removal of impulse noise based on homogeneity level information”, IEEE Trans on image processing, vol.12, no.1, pp.85-92, January 2003. [Gurmeet kaur et al 2012] Rupindar Kaur “Image De-noising using Wavelet Transform and various filters”, International journal of researcher in computer science Eissn 2249-8265 volume 2(2012)pp.15-21W. [H. Hosseini et al 2011], Senior Member, IEEE “Fast Impulse Noise Removal from highly corrupted image” [How-Lung Eng et al 2001], Kai-Kuang Ma,” Noise adaptive soft switching median filter”, IEEE Trans on Image process., vol.10, no.2, pp.242-251, Feb.2001. [Ho-Ming Lin et al 2007],Alan N Willson, “Median filters with adaptive Length”, IEEE Trans on circuits and systems, vol.35,no.6, pp.675-690, June 1988. [H. Hwang et al 1995],R. A. Haddad, "Adaptive median filters: New algorithms and results", IEEE Transactions on Image Processing, vol.4, no. 4, 1995,pp. 499-502. [H. Ibrahim et al 2008], N. S. P. Kong, and T. F. Ng, "Simple adaptive median filter for the removal of impulse noise from highly corrupted images",IEEE Transactions on Consumer Electronics, vol. 54, no. 4, 2008, pp.1920-1927 1920-1927 [H. Takeda et al 2007],S.Farsiu,P.Milanfar,”Kernel regression for image processing and reconstruction”,IEEETrans.ImageProcess.Vol.16,2007,pp- 349–366. .[Indu S and Chaveli Ramesh 2007], “A Noise Fading Technique for Images Highly Corrupted with Impulse Noise” International Conference on Computing: Theory and Applications (ICCTA'07). [ J. Seeker 1995], "A ring median filter for digital images", Astronomical Society of the Pacific, vol. 107, 1995, pp. 496-501. [J.R. Koza 1994], “Automatic Discovery of Reusable Programs”, MIT Press, Cambridge, MA, USA, 1994.

Miss. Gayatri P. Bhelke,

IJRIT

108

IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 102-110

18. [J.Mairal et al 2009], F.Bach,J.Ponce,G.Sapiro, A.Zisserman, “Non-localsparse models for image restoration” ,IEEE International Conference on Computer Vision (ICCV),,Kyoto,Japan,2009, pp. 2272–2279 19. [J. Portilla et al 2003],V.Strela,M.J.Wainwright, E.P.Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain”, IEEE Trans. Image Process.Vol.12,2003,pp-1338–1351 20. [K. Toh et al 2010],N.Isa, “Noise adaptive fuzzy switching median filter for salt- and-pepper noise reduction”,IEEE Signal Process. 2010,pp- 281–284. 21. [Luo, 2006], “Efficient Removal of Impulse Noise from Digital Images,”IEEE Transactions on Consumer Electronics, Vol. 52, No. 2, May 2006,pp. 523-527. 22. [L.Rudin et al 1994],S.Osher, “Total variation based image restoration with free local constraints”, IEEE International Conference on Image Processing(ICIP), Austin,Texas,USA,1994,pp.31–35. 23. [L. Shao et al 2013], R. Yan, X. Li, Y. Liu, “ From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denois- ing algorithm”, IEEE Trans. Cybern. 2013. 24. [L. Shao et al 2008],H.Zhang,G.deHaan, “An over view and performance evaluation of classification-based least squares trained filters” ,IEEE Trans .Image Process. Vol.17,2008,pp-1772–1782. 25. [L. Shapiro et al 2001], G. Stockman, Computer Vision, Prentice Hall, 2001. 26. [Ming yan 2011], “Restoration of Images Corrupted by Impulse Noise using Blind Inpainting and l0 Norm”. 27. [M. Elad et al 2006],M.Aharon, “Image denoising via sparse and redundant representations over learned dictionaries”,IEEETrans.ImageProcess.,Vol.15,2006,pp-3736–3745. 28. [M.Zhang et al 2008],B.Gunturk, “A new image denoising frame work based on bilateral filter”, SPIE Visual Communications and ImageProcessing,SanDiego,CA,USA,2008,pp.6822–68221B 29. [Nicholas Sia Pik Kong et al 2010 ], Haidi Ibrahim “The Effect of shape The Effect of Shape and Weight Towards the Performance of Simple Adaptive Median Filter in Reducing Impulse Noise Level from Digital Images”,2nd International Conference on Education Technology and Computer (ICETC) Vol.5 pp.118121,2010 30. [N.Petrovic et al 2006],V.Crnojevic, “Evolutionary tree-structured filter for impulse noise removal, International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS),Antwerp,Belgium,2006,pp.103–113. 31. [N.Petrovic et al 2005],V.Crnojevic, “Impulse noise detection based on robust statistics and genetic programming”, International Conference on Advanced Concepts for Intelligent Vision Systems(ACIVS),Antwerp,Belgium,2005,pp.643–649. 32. [N.I. Petrovic et al. 2008],V.Crnojevic, “Universal impulse noise filter based on genetic programming”, IEEETrans.ImageProcess. Vol.17,2008,pp-1109– 1120. 33. [P.Bouboulis et al2010],K.Slavakis,S.Theodoridis, “Adaptive kernel-based image denoising employing semiparametric regularization”,IEEE Trans.ImageProcess,.vol19,2010,pp.1465–1479. 34. [P.Chatterjee et al 2009],P.Milanfar, “Clustering-based denoising with locally learned dictionaries” ,IEEETrans.ImageProcess,vol,18,2009,pp-1438–145 35. [ Pei-Eng Ng et al 2006], Kai-Kuang Ma, “A switching median filter with BDND for extremely corrupted images”, IEEE Trans on Image process., vol. 15, no.6, pp.1506-1516, June 2006. 36. [Peter D. Wendt et al 1986], Edward J Coyle and Neal. C. Gallagher, “Some convergence properties of median filters”, IEEE Transon circuits and systems, vol. cas-33, no.3, pp.276-286, March 1986 37. [Ping et al 2007], W.; Junli, L.; Lu, D.; and Chen, G. “A Fast and Reliable Switching Median Filter for Highly Corrupted Images by Impulse Noise,” IEEE Transactions, 2007, pp. 3427-3440. 38. [P.Perona et al 1990],J.Malik,“ Scale space and edge detection using an isotropic diffusion”, IEEE Trans. Pattern Anal.Mach.Intell.,Vol.12,1990,pp-629–639. 39. [Qin Zhiyuan a et al 2010] Zhang Weiqiang a, Zhang Zhanmu a, Wu Bing b, RuiJie a,ZhuBaoshan “A Robust Adaptive image smoothing algorithm” 40. [R. Gonzales et al 2002] R. Woods “Digital Image Processing” Second Edition Prentice-Hall, Inc. 2002. 41. [Raymond H Chan et al 2005], Chung-Wa Ho and Mila Nikolova, “Salt nd pepper noise removal by median type noise detectors and detail preserving regularization”, IEEE Trans on Image Processing, vol.14, no.10, pp. 1479-1485, October 2005. 42. [Reinhard Berstein 1987], “Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images”,IEEE Trans on circuits and systems, vol.Cas-34, no.11,pp.1275-1291, November 1987. 43. [Ruomei Yan et.al 2013], LingShao, LiLiu , YanLiu, “Natural image denoising using evolved local adaptive filters”, “Elsevler science direct”,November 2013 44. [R. Poli et al 2008], W.B. Langdon, N.F. McPhee, “A Field Guide to Genetic Programming”, Lulu Enterprises, UK, 2008. 45. [R. Yan et al 2013], L. Shao, “Image blur classification and parameter identifica- tion using two-stage deep belief networks”, British Machine Vision Conference (BMVC), Bristol, UK, 2013 46. [R. Yan et al 2013],L.Shao,Y.Liu, “Non local hierarchical dictionary learning using wavelets for image denoising”, IEEE Trans, Image Process. ,2013.pp-4689–4698.

Miss. Gayatri P. Bhelke,

IJRIT

109

IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 102-110

47. [S.Schulte et. al 2006], M. Nachtegael, V. De Witte, D. Van der Weken, E.E.Kerre, “A Fuzzy Impulse Noise Detection and Reduction Method,” IEEETransactions on Image Processing, Vol 15, Issue 5, May 2006, pp.1153 - 1162. 48. [S. Schulte, M. Nachtegael et al 2006] V. De Witte, D. Van der Weken, E.E. Kerre, “Fuzzy Two-Step Filter for Impulse Noise Reduction From Color Images,”IEEE Transactions on Image Processing” Vol 15, No 11, November 2006, pp.3568-3579. 49. [Shyam Lall et al 2009] Mahesh Chandra, Gopal Krishna Upadhya “Noise Removal Algoritham for Image Corrupted by Additive Gaussian Noise”,International Journal of Recent trends in Engineering,vol 2,No.1,November 2009,pp199-206 50. [S. J. Ko et al 1991], Y. H. Lee, "Center weighted median filters and their applications to image enhancement", IEEE Transactions on Circuits and Systems, vol. 38, no. 9, 1991, pp. 984-993. 51. [S. Perrault et al 2007], P. Herbert, "Median filtering in constant time", IEEE Transactions on Image Processing, vol. 16, no. 9, 2007, pp. 2389-2394. 52. [ S. T. Boo et al 2009], H. Ibrahim, and K. K. V. Toh, "An improved progressive 53. switching median filter", In Proceedings of the International Conference on Future Computer and Communications (lCFCC 2009),International Association of Computer Science and Information Technology (IACSIT), Conference Publishing Services (CPS), The MINES, Selangor, Malaysia, 2009, pp. 136-139. 54. [S.J. Ko et al 1991],Y.H.Lee, “Center weighted median filters and their applications to image enhancement”, IEEE Trans. Circuits Syst. Vol.38,1991,pp- 984–993. 55. [S. Zhang et al 2002], M. A. Karim, "A new impulse detector for switching median filters", IEEE Signal Processing Letters, vol. 9, no. 11,2002, pp. 360-363. 56. [S.M. Smith et al 1997],J.M.Brady, Susan, “A new approach to low level image processing”, Int.J.Comput.Vis. Vol.23,1997,pp-45–78. 57. [T. Chen et al 2006] S. Huang, C. Chen, and Z. Lin, “Adaptive Working Window for Impulse Noise Reduction,” International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2006. 58. [Tao Chen et al 1999], Kai-Kuang Ma , Li- Hui- Chen, “Tristate median filter for image denoising”, IEEE Trans on image processing, vol.8, no.12, pp.1834-1838, December 1999. 59. [T.Thaipanich et al 2010],O.Byung Tae,W.Ping-Hao,X.Daru,C.C.J.Kuo, “ Improve dimage denoising with a daptive non local means (anl-means) algorithm, IEEE Trans.Consum.Electron. Vol.56,2010,pp- 2623–2630 60. [V. Saradhadevi et. al 2011], Dr.V.Sundaram, “An Enhanced Two-Stage Impulse Noise Removal Technique based on Fast ANFIS and Fuzzy Decision”, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 1, September 2011. 61. [W. Luo et al 2006 ] D. Dang, “An Efficient Method for the Removal of Impulse Noise,” ICIP, 2006, pp. 2601-2604 62. [W. Luo,2006] “An Efficient Detail-Preserving Approach for Removing Impulse Noise in Images,” In IEEE Siganl Processing Letters, Vol 13, No 7, July 2006, pp. 413-416. 63. [W. Luo,2006] “Efficient Removal of Impulse Noise from Digital Images,”IEEE Transactions on Consumer Electronics, Vol. 52, No. 2, May 2006,pp. 523-527 64. [Xiaoyin Xu et al 2004], Eric L Miller, Dong bin Chen and Mansoor Sarhadi, “Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images”, IEEE Trans on image processing, vol.13, no.2, pp. 238-247, February 2004 65. [Xu, H et al 2006], Xia, X.; Guo, L.; Chen, W.; and Huang, G. “Classification-based Weighted Filter for Image Corrupted by Impulse Noise,” ICSP Proceedings, 2006. 66. [Z. Wang et al 2004],A.C.Bovik, H.R.Sheikh, E.P.Simoncelli, “Image quality assessment: from error visibility to structural similarity” ,IEEE Trans. Image Process. Vol.13,2004,pp-600–612.

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