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An edge-preserving adaptive image denoising

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Abstract

Based on nonsubsampled shearlet transform (NSST) and fuzzy support vector machines (FSVMs), we present a new denoising approach that can effectively suppress noise from an image while keeping its features intact. The noisy image is firstly decomposed into different subbands of frequency and orientation responses using NSST. The NSST detail coefficients are then divided into edge/texture-related coefficients and noise-related ones by FSVMs classifier. And finally the detail subbands of NSST coefficients are denoised by using the adaptive Bayesian threshold. Extensive experimental results demonstrate that our approach is competitive relative to many state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.

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References

  1. Abbas K, Ulugbek SK, Emrah B, Michael U (2013) Bayesian denoising: from MAP to MMSE using consistent cycle spinning. IEEE Signal Process Lett 20(3):249–252

    Article  Google Scholar 

  2. Ashkezari AD, Ma H, Saha TK, Ekanayake C (2013) Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers. IEEE Trans on Dielectr and Electr Insul 20(3):965–973

    Article  Google Scholar 

  3. Balste EJ, Zheng YF, Ewing RL (2006) Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising. IEEE Trans on Circ and Systems for Video Technol 16(2):220–230

    Article  Google Scholar 

  4. Balster EJ, Zheng YF, Ewing RL (2005) Feature-based wavelet shrinkage algorithm for image denoising. IEEE Trans Image Process 14(12):2024–2039

    Article  Google Scholar 

  5. Beck A, Teboulle M (2009) Fast gradient-based algorithm for constrained total variation image denoising and deblurring problems. IEEE Trans Image Process 18(11):2419–2434

    Article  MathSciNet  Google Scholar 

  6. Bhujle H, Chaudhuri S (2014) Novel speed-up strategies for non-local means denoising with patch and edge patch based dictionaries. IEEE Trans Image Process 23(1):356–365

    Article  MathSciNet  Google Scholar 

  7. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. 2005 International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, 2: 60–65

  8. Dabov K, Foi A, Katkovnik V (2007) Image denoising by sparse 3d transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095

    Article  MathSciNet  Google Scholar 

  9. Dai W, Yu S, Sun S (2007) Image de-noising algorithm using adaptive threshold based on contourlet transform. Acta Electron Sin 35(10):1939–1943

    Google Scholar 

  10. Easley G, Labate D, Lim WQ (2008) Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal 25(1):25–46

    Article  MathSciNet  MATH  Google Scholar 

  11. Hou B, Zhang X, Bu X (2012) SAR image despeckling based on nonsubsampled shearlet transform. IEEE J of Sel Top in Appl Earth Obs and Remote Sens 5(3):809–823

    Article  Google Scholar 

  12. Karacan L, Erdem E, Erdem A (2013) Structure-preserving image smoothing via region covariances. ACM Trans Graph 32(6):176. doi:10.1145/2508363.2508403

    Article  Google Scholar 

  13. Krissian K, Aja-Fernández S (2009) Noise-driven anisotropic diffusion filtering of MRI. IEEE Trans Image Process 18(10):2265–2274

    Article  MathSciNet  Google Scholar 

  14. Lim WQ (2010) The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans Image Process 19(5):1166–1180

    Article  MathSciNet  Google Scholar 

  15. Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans on Neural Netw 13(2):464–471

    Article  Google Scholar 

  16. Liu C, Szeliski R, Kang SB (2008) Automatic estimation and removal of noise from a single image. IEEE Trans on Pattern Anal and Mach Intell 30((2):299–3142

    Article  Google Scholar 

  17. Luca F, Mario G, Marco M, Gianpaolo P (2014) Improved edge enhancing diffusion filter for speckle-corrupted images. IEEE Geosci Remote Sens Lett 11(1):99–103

    Article  Google Scholar 

  18. Luisier F, Blu T, Unser M (2007) A new SURE approach to image denoising: interscale orthonormal wavelet thresholding. IEEE Trans Image Process 16(3):593–606

    Article  MathSciNet  Google Scholar 

  19. Masoud H, Soosan B (2014) Adaptive Bayesian denoising for general gaussian distributed signals. IEEE Trans on Signal Process 62(5):1147–1156

    Article  Google Scholar 

  20. Peleg T, Eldar YC, Elad M (2012) Exploiting statistical dependencies in sparse representations for signal recovery. IEEE Trans on Signal Process 60(5):2286–2303

    Article  MathSciNet  Google Scholar 

  21. Pizurica A, Philips W (2006) Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising. IEEE Trans Image Process 15(3):645–665

    Article  Google Scholar 

  22. Portilla J, Strela V, Wainwright MJ, Simoncelli EP (2003) Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans Image Process 12(11):1338–1351

    Article  MathSciNet  MATH  Google Scholar 

  23. Rabbani H, Gazor S (2010) Image denoising employing local mixture models in sparse domains. IET Image Process 4(5):413–428

    Article  Google Scholar 

  24. Rajwade A, Rangarajan A, Banerjee A (2013) Image denoising using the higher order singular value decomposition. IEEE Trans on Pattern Anal and Mach Intell 35(4):849–862

    Article  Google Scholar 

  25. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D: Nonlinear Phenom 60(1–4):259–268

    Article  MATH  Google Scholar 

  26. Takeda H, Farsiu S, Milanfar P (2007) Kernel regression for image processing and reconstruction. IEEE Trans Image Process 16(2):349–366

    Article  MathSciNet  Google Scholar 

  27. Tsiotsios C, Petrou M (2013) On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recogn 46(5):1369–1381

    Article  Google Scholar 

  28. Wang XY, Yang HY, Fu ZK (2010) A new wavelet-based image denoising using undecimated discrete wavelet transform and least squares support vector machine. Expert Syst with Appl 37(10):7040–7049

    Article  Google Scholar 

  29. Yi S, Labate D, Easley GR (2009) A shearlet approach to edge analysis and detection. IEEE Trans Image Process 18(5):929–941

    Article  MathSciNet  Google Scholar 

  30. Zhang M, Gunturk BK (2008) Multiresolution bilateral filtering for image denoising. IEEE Trans Image Process 17(12):2324–2333

    Article  MathSciNet  Google Scholar 

  31. Zhang K, Lafruit G, Lauwereins R (2012) Constant time joint bilateral filtering using joint integral histograms. IEEE Trans Image Process 21(9):4309–4314

    Article  MathSciNet  Google Scholar 

  32. Zhu X, Milanfar P (2013) Removing atmospheric turbulence via space-invariant deconvolution. IEEE Trans on Pattern Anal and Mach Intell 35(1):157–170

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61472171 & 61272416, the Open Project Program of Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) under Grant No. 30920130122006, the Open Foundation of Zhejiang Key Laboratory for Signal Processing under Grant No. ZJKL_4_SP-OP2013-01, the Open Foundation of Provincial Key Laboratory for Computer Information Processing Technology (Soochow University) under Grant No. KJS1325, the Open Project Program of the State Key Lab of CAD&CG (Grant No. A1425), Zhejiang University, and Liaoning Research Project for Institutions of Higher Education of China under Grant No. L2013407.

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Authors and Affiliations

  1. School of Computer and Information Technology, Liaoning Normal University, Dalian, 116029, People’s Republic of China

    Xiang-Yang Wang, Yang-Cheng Liu, Na Zhang, Chang-Jian Wu & Hong-Ying Yang

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Soochow, 215006, People’s Republic of China

    Xiang-Yang Wang

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  1. Xiang-Yang Wang
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  2. Yang-Cheng Liu
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  3. Na Zhang
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  4. Chang-Jian Wu
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  5. Hong-Ying Yang
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Correspondence to Xiang-Yang Wang or Hong-Ying Yang.

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Wang, XY., Liu, YC., Zhang, N. et al. An edge-preserving adaptive image denoising. Multimed Tools Appl 74, 11703–11720 (2015). https://doi.org/10.1007/s11042-014-2258-x

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  • DOI: https://doi.org/10.1007/s11042-014-2258-x

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