Spatial and Transform Domain Filtering Method for Image De-noising: A Review

Spatial and Transform Domain Filtering Method for Image De-noising: A Review

Автор: Vandana Roy, ShailjaShukla

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 7 vol.5, 2013 года.

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Present investigation reveals the quantum of work carried in the filtering methods for image de-noising. An image is often gets corrupted by various noises that are visible or invisible while being gathered, coded, acquired and transmitted. Noise influences various process parameters that may cause a quality problem for further image processing. De-noising of natural images is appears to be very simple however when considered under practical situations becomes complex. It has been cited by various author that parameter such as type and quantum of noise, image etc. through single algorithm or approach becomes cumbersome when results are optimized. In order to improve the quality of an image noise must be removed when the image is pre-processed and the important signal features like edge details should be retained as much as possible. The search on efficient image de-noising methods is still a valid challenge at the crossing of functional analysis and statistics. This paper reviews significant de-noising methods (spatial and transform domain method) and their salient features and applications. One filter in each category has been taken in consideration to understand the characteristics of both spatial and transform domain filters.

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Median Filter, Weiner Filter, Wavelet Transform, ICA

Короткий адрес: https://sciup.org/15014566

IDR: 15014566

Список литературы Spatial and Transform Domain Filtering Method for Image De-noising: A Review

  • Motwani M.C., Gadiya M. C., Motwani R.C., Harris F.C, “Survey of Image de-noising technique”.
  • Patidar P., Srivastava S., Gupta M., Nagawat A.K. “Image De-noising by Various Filters for Different Noise”,2010, International Journal of Computer Applications, 9(4), 45-50.
  • Suryanarayana S, Deekshatulu B.L., Kishore K.L., RakeshKumar Y. “Novel Impulse Detection Technique For Image Denoising”,2009, Journal of Theoretical and Applied Information Technology, 102-106.
  • Chang C.C., Hsiao J.Y., Hsieh C.P.,2008, “An Adaptive Median Filter for Image Denoising” Second International Symposium on Intelligent Information Technology Application, IEEE,346-350.
  • Aboshosha A., Hassan M., Ashour M., Mashade M. EI, “Image Denoising based on Spatial Filters, an Analytical Study”, 2009,International conference on computer science and engineering,IEEE, 245-250.
  • Gui-quan XI， Wei-zhen S. ， Liang MA, “Mixed Wavelet and Median Filter for Image Denoising”, 2009,international conference on computational intelligence and software engineering, IEEE, 1-4.
  • Jiang J., Shen J., “An Effective Adaptive Median Filter Algorithm for Removing Salt & Pepper Noise in Images”,2010,Symposium on photonics and optoelectronics, IEEE, 1-4.
  • Qiu Y. Gan Z., Fan Y., Zhu X. , “An Adaptive Image Denoising Method for Mixture Gaussian Noise”,2011,international conference on wireless communication and signal processing, IEEE ,1-5.
  • RatnaBabu K., Arun Rahul L., Souri P.V., Suneetha A., “Image Denoising in the Presenseof High Level Salt and Pepper Noise using Modified Median Filter”, 2011, IJCST ,2(1), 180-183.
  • Ma Y. , Li J. , “A Novel Method based on Adaptive Median Filtering and Wavelet Transform in noise images” ,2011,international conference on communication software and networks , IEEE,626-629.
  • Roy V.,shailja S., “Performance Evaluation of Different Image Denoising Methods” ,2012,International journal of electronics and computer science engineering,1(3),1757-1764.
  • Selesnick I.W., “A New Complex-Directional Wavelet Transform And Its Application To Image Denoising”, 2002, international conference on image processing, IEEE, 2002, vol 3,573-576.
  • Li Y., Lu J., Wang L., and Yahagi T., “Removing Poisson Noise from Images in Wavelet Domain”, 2005, international conference on industrial technologies, IEEE, 39-44.
  • Balster E.J.,Zheng Y.F. and Ewing R.L., “Feature-Based Wavelet Shrinkage Algorithm for Image Denoising”,2005,IEEE Transactions on Image Processing, 14 ( 12), 2024-2039.
  • Patel N.M., Modi C.K. And Rathod R.J., “An Improved Adaptive Wavelet Threshold for Image Denoising”, 2006, National Conference on Current Trends In Technology, 475-477.
  • Xueyan L., Shuxu G., Ye L., Jingwei F., Shuai J., “1/f Fractal Signals Denoising with Dual-Tree Complex Wavelet Transform”,2006,Joint Conference On Information Sciences.
  • Yu Y., Yang J., “A New Method of Image Feature Extraction and Denoising Based on Independent Component Analysis”, 2006, International Conference on Robotics and Biomimetics,IEEE.
  • Bo D.C., ZexunG.,Yang Y., Tianshuang S., “Dual-tree Complex Wavelets Transforms for ImageDenoising”,2007,International conference on software engineering, artificial intelligence, networking, parallel/distributed computing ,IEEE vol. 1 ,70-74.
  • Pragada S. and Sivaswamy J., “Image denoising using matched biorthogonal wavelets”, 2008, Sixth Indian Conference on Computer Vision, Graphics & Image Processing , IEEE, 25-32.
  • En-hai L., Hong-pu L., Yan Z., Zhi-tao G., “An Improved Method of Image Denoising Base on Stationary Wavelet”,2009, Global Congress on Intelligent Systems IEEE, 379-383.
  • Hongqiao L., Shengqian W., “A New Image Denoising Method Using Wavelet Transform”, 2009, International conference on information technology and application,IEEE,vol. 1, 111-114.
  • Ellinas J.N., Mandadelis T., Tzortzis A., Aslanoglou L., “Image de-noising using wavelets” T.E.I. of Piraeus, 1 -13.
  • Kaur L., Gupta S., Chauhan R.C. , “Image De-noising using Wavelet Thresholding”.
  • Marusic S., Deng G. and Tay D.B.H., “Image De-noising Using Over-Complete Wavelet Representations”, Victorian Partnership for Advanced Computing (VPAC) Australia; UNSW, Australia.
  • Sudha S., Suresh G.R. and Sukanesh R., “Speckle Noise Reduction in Ultrasound Images by Wavelet Thresholding”, 2009, International Journal of Computer Theory and Engineering,1(1), 7- 12.
  • Zhou S.,Xingzho, Xie W., “A Modified Image Denoising Algorithm by Labeling and 3D Wavelet Transform”, 2010,International Conference on Computer Application and System Modeling (ICCASM 2010),IEEE, v13-44 to v13-47.
  • Shuang-ping Z., “A Combined Image Denoising Method”, 2010, International conference on image analysis and signal processing(IASP),IEEE,254-256.
  • Arfia F.B., Messaoud M.B., Abid M., “A New Image denoising Technique Combining the Empirical Mode Decomposition with a Wavelet Transform Technique” ,2010,International Conference On Systems, Signals And Image Processing, Iwssip, 514-517.
  • Ruikar S.D., Doye D.D., “Wavelet Based Image Denoising Technique”, 2011, International Journal of Advanced Computer Science and Applications,2(3),49 -53.
  • Ram I., EladM.,Cohen I., “Generalized Tree-Based Wavelet Transform”,2011,IEEE Transactions on Signal Processing, 59(9),4199-4210.
  • Fathi A. and Nilchi A.R.N., “An Efficient Image Denoising Method Based on a New Adaptive Wavelet PacketThresholdingFunction”, 2012,IEEE Transaction of Image Processing, 21 (9), 3981-3990.
  • Laparra V., Camps-Valls G. and Malo J., “PcaGaussianizationfor Image Processing”, 2009, International Conference on Image Processing, IEEE, 3985-3988.
  • Emir U.E., Akgül C.B., Akın A., Ertüzün A., Sankur B. andHarmancı K., “Wavelet Denoisingvs Ica Denoisingfor Functional Optical Imaging”, 2003, International Conference on Neural Engineering,IEEE.
  • Mayo P., Rodenas F., Verdú G., “Comparing Methods to Denoise Mammographic Images”, 2004,Annual International Conference of the IEEE Embs, san Francisco,CA,USA 247-250.
  • Zhao H., zhou W., peng Y., “Application of Independent Component Analysis on Noisy Image Separation”, 2004, International Conference on Signal Processing, 1018-1021.
  • Gruber P., Theis F.J., Stadlthanner K.,Lang E.W., Tomc A.M., Teixeira A.R., “Denoisingusing Local ICA and Kernel-PCA” , 2004, International Joint Conference on Neural Network,IEEE,vol. 3, 2071-2076.
  • Huang Z.H., “Image Denoising By Independent Component Analysis Based On Dyadic Wavelet Transform”,2009,International Conference on Wavelet Analysis and Pattern Recognition, Baoding,IEEE. 351-354.
  • Bobin J., Moudden Y., Starck J.L., andElad M., “Morphological Diversity and Source Separation”, 2006, Signal Processing Letter,IEEE, 13(7), 409-412.
  • Shiqun J., Qiaoyun L., Youqiang Z., “The image denoising technique based on independent component analysis”, 2008 ,Fourth International Symposium on Precision Mechanical Measurements SPIE 2008, vol. 7130, 71304I-1 to 713041-6.
  • Khaparde A.,Madhavilatha M.,Manasa M.B.L., Babu P.A., Pradeep Kumar S., “FastICA Algorithm for the Separation of Mixed Images”,2008,Wseas Transactions on Signal Processing, 5(4), 271-278.
  • Li H.Y., Ren G.L., Xiao B.J., “Image Denoising Algorithm Based on Independent Component Analysis”, 2009,World Congress on Software Engineering ,IEEE,vol. 4, 465-469.
  • Shang L., Zhang J.F., Huai W. , Chen J., “Natural Image Denoising Using Sparse ICA Based on 2-D Gabor Wavelet”, 2009, Natural Image De-noising Uisng Sparse ICA Based on 2-D Gabor Wavelet,CISP.
  • Jicheng L., Yi Z., “Image Denoising based on Independent Component Analysis”, 2009, International Conference on Intelligent Human-Machine Systems and Cybernetics, 18-20.
  • Kakumani A., Mendhurwar K.A., Kakumani R. , “Microarray Image Denoising using Independent Component Analysis”,2010, international journal of computer applications, 1(11) ,1-7.
  • Potnis A., Somkuwar A. and Sapre S.D., “A Review on Natural Image Denoising using Independent Component Analysis (Ica) Technique”, 2010, Advances in Computational Research, Volume 2(1), 06-14.
  • ArakeriM.P.,Ram G., Reddy M., “A Comparative Performance Evaluation of Independent Component Analysis in Medical Image Denoising”,2011, International conference on recent trends in information technology , 770-774.
  • Boroomand A., Ahmadian A., Oghabian M.A., Alirezaie J., Beckman C., “An Efficient Hybrid Wavelet-ICA algorithm for Analyzing Simulated fMRI Data in Noisy Environment”, 2007,International Symposium on Signal Processing and Information Technology,IEEE, 408-413.
  • Zhang S., Tang T., Wu C., Xi N., Wang G, “A Novel Image Denoising Method Using Independent Component Analysis And Dual-Tree Complex Wavelet Transform”,2010,International Conference on Wireless Communication Networking And Mobile Computing,IEEE, 1-4.
  • Vazquez R.R.,Vélez-Péreza H., Ranta R., Dorr V.L., Maquin D., Maillard L., “Blind Source Separation, Wavelet Denoising And Discriminant Analysis for EEG Artefacts and Noise Cancelling”, 2011, Biomedical Signal Processing and Control xxx (2011) xxx– xxx, 1- 12.
  • Geethaa G., Geethalakshmia S.N., “Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu’s Thresholding Technique”, 2011, International Conference on Communication Technology and System Design ,1064-1071.
  • Williams J.W. & Li Y., “A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wavelet and ICA”, 2011,International Journal of Biometrics and Bioinformatics,5(2),130 - 148.
  • Roy V., Shukla S., “Magnetic Resonance Image Denoising& Enhancement using Maximum Likelihood ICA (ML-ICA) & DWT” ,2012,International journal of Advance in Science and technology,4(5),122-129.
  • Yang B. And Jing Z., “A Simple Method to Build Oversampled Filter Banks and Tight Frames”, 2007,IEEE Transactions on Image Processing, 16(11), 2682-2687.
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