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A survey of edge-preserving image denoising methods

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Abstract

Reducing noise has always been one of the standard problems of the image analysis and processing community. Often though, at the same time as reducing the noise in a signal, it is important to preserve the edges. Edges are of critical importance to the visual appearance of images. So, it is desirable to preserve important features, such as edges, corners and other sharp structures, during the denoising process. This paper presents a review of some significant work in the area of image denoising. It provides a brief general classification of image denoising methods. The main aim of this survey is to provide evolution of research in the direction of edge-preserving image denoising. It characterizes some of the well known edge-preserving denoising methods, elaborating each of them, and discusses the advantages and drawbacks of each. Basic ideas and improvement of the denoising methods are also comprehensively summarized and analyzed in depth. Often, researchers face difficulty in selecting an appropriate denoising method that is specific to their purpose. We have classified and systemized these denoising methods. The key goal of this paper is to provide researchers with background on a progress of denoising methods so as to make it easier for researchers to choose the method best suited to their aims.

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References

  • Abramovitch, F., Sapatinas, T., & Silverman, B. W. (1998). Wavelet thresholding via a Bayesian approach. Journal Roy Statistic Society B, 60(4), 725–749.

    Article  Google Scholar 

  • Aurich, V. & Weule, J. (1995). Non-linear Gaussian filters performing edge preserving diffusion. In Proc. DAGM Symp., Bielefeld, Germany, 538–545.

  • Bae, S., Paris, S. & Durand, F. (2006). Two-scale tone management for photographic look. SIGGRAPH, 637–645.

  • Bao, Q., Gao, J., & Chen, W. (2008). Local adaptive shrinkage threshold denoising using curvelet coefficients. Electronics Letters, 44(4), 277–279.

    Article  Google Scholar 

  • Ben Hamza, A., Luque, P., Martinez, J., & Roman, R. (1999). Removing noise and preserving details with relaxed median filters. Journal of Matter Image and Visualization, 11(2), 161–177.

    Google Scholar 

  • Benazza-Benyahia, A., & Pequet, J.-C. (2005). Building robust wavelet estimators for multicomponent images using Stein’s principle. IEEE Transactions on Image Processing, 14(11), 1814–1830.

    Article  Google Scholar 

  • Benesty, J., Cheng J. & Huang, Y. A. (2010). Study of widely linear Wiener filter for noise reduction. In Proc. IEEE Conf. ICASSP., 205–208.

  • Black, M. J., Sapiro, G., Marimont, D. H., & Heeger, D. (1998). Robust anisotropic diffusion. IEEE Transactions on Image Processing, 7(3), 421–432.

    Article  Google Scholar 

  • Blu, T., & Luisier, F. (2007). The SURE-LET approach to image denoising. IEEE Transactions on Image Processing, 16(11), 2778–2786.

    Article  Google Scholar 

  • Buades, A., Coll, B., & Morel Song, J. M. (2005). A review of image denoising algorithms, with a new one. SIAM Journal on Multiscale Modeling and Simulation, 4(2), 490–530.

    Article  Google Scholar 

  • Chambolle, A. (2004). An algorithm for total variation minimization and applications. Journal of Matter Image and Visualization, 20, 89–97.

    Google Scholar 

  • Chang, S., Yu, B., & Vetterli, M. (2000a). Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing, 9(9), 1532–1546.

    Article  Google Scholar 

  • Chang, S., Yu, B., & Vetterli, M. (2000b). Spatially adaptive wavelet thresholding based on context modeling for image denoising. IEEE Transactions on Image Processing, 9(9), 1522–1531.

    Article  Google Scholar 

  • Chaudhury, K. N. (2013). Acceleration of the shiftable O(1) algorithm for bilateral filtering and nonlocal means. IEEE Transactions on Image Processing, 22(4), 1291–1300.

    Article  Google Scholar 

  • Chaudhury, K. N., Sage, D., & Unser, M. (2011). Fast O(1) bilateral filtering using trigonometric range kernels. IEEE Transactions on Image Processing, 20(12), 3376–3382.

    Article  Google Scholar 

  • Chipman, H., Kolaczyk, E., & McCulloch, R. (1997). Adaptive Bayesian wavelet shrinkage. Journal American Statistics Association, 440(92), 1413–1421.

    Article  Google Scholar 

  • Choi, H., & Baraniuk, R. (1998). Analysis of wavelet-domain wiener filters (613–616). Pittsburgh, PA, USA: IEEE international symposium on time-frequency and time-scale analysis.

    Google Scholar 

  • Choux, C., Duval, L., Benazza-Benyahia, A., & Pequet, J.-C. (2008). A nonlinear Stein-based estimator for multichannel image denoising. IEEE Transactions on Image Processing, 56(8), 3855–3869.

    Article  Google Scholar 

  • Combettes, P. L., & Pesquet, J.-C. (2004). Wavelet-constrained image restoration. International Journal Wavelets, Multires. Information Process, 2(4), 371–389.

    Article  Google Scholar 

  • da Silva, R. D., Minetto, R., Schwartz, W. R., & Pedrini, H. (2012). Adaptive edge-preserving image denoising using wavelet transforms (Pattern analysis and applications, 1–14). Berlin: Springer.

    Google Scholar 

  • Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8), 2080–2095.

    Article  Google Scholar 

  • Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.

    Article  Google Scholar 

  • Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation via wavelet shrinkage. Biometrika, 81, 425–455.

    Article  Google Scholar 

  • Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432), 1200–1224.

    Article  Google Scholar 

  • Durand, F., & Dorsey, J. (2002). Fast bilateral filtering for the display of high dynamic range images. ACM Transactions on Graphics, 21(3), 257–266.

    Article  Google Scholar 

  • Elad, M. (2002). On the origin of the bilateral filter and ways to improve it. IEEE Transactions on Image Processing, 11(10), 1141–1151.

    Article  Google Scholar 

  • Fan, H., Peng, Q., & Yu, Y. (2013). A robust high resolution details preserving denoising algo for meshes. Science China Information Sciences, 56(9), 1–12.

    Google Scholar 

  • Farbman, Z., Fattal, R., Lischinski, D., & Szeliski, R. (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics, 27(3), 1–10.

    Article  Google Scholar 

  • Fodor, I. K. & Kamath, C. (2001). Denoising through wavlet shrinkage: An empirical study. Center for applied science computing Lawrence Livermore National Laboratory.

  • Ghael, S., Ghael, E. P., Sayeed, A. M., & Baraniuk, R. G. (1997). Improved wavelet denoising via empirical wiener filtering (3169th ed. Proceedings of SPIE San Diego 389–399.

    Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (2006). Digital image processing. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Hardie, R. C., & Barner, K. E. (1994). Rank conditioned rank selection filters for signal restoration. IEEE Transactions Image Processing, 3, 192–206.

    Article  Google Scholar 

  • He, K., Sun, J. & Tang, X. (2010). Guided image filtering. In Proc. Eur. Conf. Comput. Vis., 1–14.

  • Ho J. & Hwang, W-L. (2013). Wavelet Bayesian Network Image Denoising. IEEE Trans. Image Process., vol. 22, no. 4, 1277–1290.

  • Hyvärinen, A., Oja, E., Hoyer, P., & Hurri, J. (1998). Image feature extraction by sparse coding and independent component analysis (Proc. Int. Conf. on pattern recognition, pp. 1268–1273). Brisbane: ICPR’98.

    Google Scholar 

  • Jain, A. K. (1989). Fundamentals of digital image processing. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Jain, P., & Tyagi, V. (2013). Spatial and frequency domain filters for restoration of noisy images. IETE Journal Education, 54(2), 108–116.

    Article  Google Scholar 

  • Jain, P. & Tyagi, V. (2014). An adaptive edge-preserving image denoising technique using tetrolet transforms, The Visual Computer, 1–18, doi http://dx.doi.org/10.1007/s00371-014-0993-7.

  • Jung, A. (2001). An introduction to a new data analysis tool: Independent component analysis (Proceedings of workshop GK). Regensburg: Nonlinearity.

    Google Scholar 

  • Kazubek, M. (2003). Wavelet domain image denoising by thresholding and wiener filtering. IEEE Signal Process Letter, 10(11), 324–326.

    Article  Google Scholar 

  • Kheelah, F. (2013). Application of LBP to windowd Nonlocal Image denoising, image analysis and processing. ICIAP 2013, Lecture Notes in Computer Science, 8156, 21–30.

    Google Scholar 

  • Levin, A., Lischinski, D., & Weiss, Y. (2008). A closed-form solution to natural image matting. IEEE Transactions Pattern Analysis and Machine Intelligence, 30(2), 228–242.

    Article  Google Scholar 

  • Luisier, F., & Blu, T. (2008). SURE-LET multichannel image denoising: Interscale orthonormal wavelet thresholding. IEEE Transactions on Image Processing, 17(4), 482–492.

    Article  Google Scholar 

  • Luisier, F., Blu, T., & Unser, M. (2007). A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding. IEEE Transactions on Image Processing, 16(3), 593–606.

    Article  Google Scholar 

  • Luisier, F., Blu, T., & Unser, M. (2011). Image denoising in mixed poisson-Gaussian noise. IEEE Transactions on Image Processing, 20(3), 696–708.

    Article  Google Scholar 

  • Maggioni, M., Katkovnik, V., Egiazarian, K., & Foi, A. (2013). Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Transactions on Image Processing, 22(1), 119–133.

    Article  Google Scholar 

  • Malfati, M., & Roose, D. (1997). Wavelet-based image denoising using a Markov random field a priori model. IEEE Transactions on Image Processing, 6(4), 549–565.

    Article  Google Scholar 

  • Mihcak, M. K., Kozintsev, I., Ramchandran, K., & Moulin, P. (1999). Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Process Letter, 6(12), 300–303.

    Article  Google Scholar 

  • Nason, G. P. (1996). Wavelet shrinkage by cross-validation. Journal Roy Statistic Society, 58, 463–479. Series B.

    Google Scholar 

  • Paris S. & Durand, F. (2006). A fast approximation of the bilateral filter using signal processing approach. In Proc. Eur. Conf. Comput. Vis., pp. 568–580.

  • Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions Pattern Analysis Machine Intelligent, 12(7), 629–639.

    Article  Google Scholar 

  • Pitas, I., & Venetsanooulos, A. (1990). Nonlinear digital filters: Principles and applications. Boston: Kluwer.

    Book  Google Scholar 

  • Pizˇurica, A., & Philips, W. (2006). Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising. IEEE Transactions on Image Processing, 15(3), 654–665.

    Article  Google Scholar 

  • Porikli, F. (2008). Constant time O(1) bilateral filtering. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Anchorage, AK, 1–8.

  • Portilla, J., Strela, V., Wainwright, M., & Simoncelli, E. (2003). Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE Transactions on Image Processing, 12(11), 1338–1351.

    Article  Google Scholar 

  • Qiu, T., Wang, A., Yu, N., & Song, A. (2013). LLSURE: Local linear SURE-based edge-preserving image filtering. IEEE Transactions on Image Processing, 22(1), 80–90.

    Article  Google Scholar 

  • Rudin, L., Osher, S., & Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D, 60, 259–268.

    Article  Google Scholar 

  • Ruggeri, F., & Vidakovic, B. (1998). A Bayesian decision theoretic approach to wavelet thresholding. Journal American Statistical Association, 93, 173–179.

    Article  Google Scholar 

  • Sendur, L., & Selesnick, I. W. (2002a). Bivariate shrinkage with local variance estimation. IEEE Signal Process Letter, 9(12), 438–441.

    Article  Google Scholar 

  • Sendur, L., & Selesnick, I. W. (2002b). Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing, 50(11), 2744–2756.

    Article  Google Scholar 

  • Sethian, J. A. (1999). Level set methods and fast marching methods: Evolving interfaces in computational geometry, fluid mechanics, computer vision and materials sciences. Cambridge: Cambridge University Press.

    Google Scholar 

  • Smith, S. M., & Brady, J. M. (1997). SUSAN—A new approach to low level image processing. International Journal of Computer Vision, 23(1), 45–78.

    Article  Google Scholar 

  • Strela, V. (2000). Denoising via block Wiener filtering in wavelet domain (3rd European congress of mathematics). Barcelona: Birkhäuser Verlag.

    Google Scholar 

  • Tomasi, C. & Manduchi, R. (1998). Bilateral filtering for gray and color images. In Proc. 6th Int. Conf. Comput. Vis., Bombay, India, pp. 839–846.

  • Van De Ville, D., & Kocher, M. (2009). SURE-based non-local means. IEEE Signal Process Letter, 16(11), 973–976.

    Article  Google Scholar 

  • Van De Ville, D., & Kocher, M. (2011). Nonlocal means with dimensionality reduction and SURE-based parameter selection. IEEE Transactions on Image Processing, 20(9), 2683–2690.

    Article  Google Scholar 

  • Vidakovic, B. (1998). Nonlinear wavelet shrinkage with Bayes rules and Bayes factors. Journal American Statistic Association, 93(441), 73–179.

    Article  Google Scholar 

  • Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  • Weaver, J., Yansun, X., & Cromwell, D. H. L. (1991). Filtering noise from images with wavelet transforms. Magnetic Resonance in Medicine, 21(2), 288–295.

    Article  Google Scholar 

  • Weickert, J., ter Haar Romeny, B. M., & Viergever, M. A. (1998). Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Transactions on Image Processing, 7(3), 398–410.

    Article  Google Scholar 

  • Weyrich, N., & Warhola, G. T. (1998). Wavelet shrinkage and generalized cross validation for image denoising. IEEE Transactions on Image Processing, 7(1), 82–90.

    Article  Google Scholar 

  • Yang, R., Yin, L., Gabbouj, M., Astola, J., & Neuvo, Y. (1995). Optimal weighted median filters under structural constraints. IEEE Transactions Signal Processing, 43, 591–604.

    Article  Google Scholar 

  • Yang, Q., Tan, K. H. & Ahuja, N. (2009). Real-time O(1) bilateral filtering. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Miami, FL, 557–564.

  • Zhang, H., Nosratinia, A., & Wells, R. O., Jr. (2000). Image denoising via wavelet-domain spatially adaptive FIR Wiener filtering. Istanbul, Turkey: In IEEE Proc Int. Conf. Acoust Speech, Signal Processing. 2179–2182

  • Zhou, C., Shufen, L., etal. (2013). Image denoising algo based on edge-preserving self-snake model and wavelet-based PDE. Intelligent Computing Theories, Lecture Notes in Computer Science, 7995, 490–497.

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  1. Research Scholar, Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, India

    Paras Jain

  2. Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

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  1. Paras Jain
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  2. Vipin Tyagi
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Correspondence to Vipin Tyagi.

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Jain, P., Tyagi, V. A survey of edge-preserving image denoising methods. Inf Syst Front 18, 159–170 (2016). https://doi.org/10.1007/s10796-014-9527-0

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