Abstract
Denoising of images is one of the most basic tasks of image processing. It is a challenging work to design a edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method, it not only can exactly compute the shearlet coefficients based on a multiresolution analysis, but also can provide nearly optimal approximation for a piecewise smooth function. Based on extended DST, an image denoising using fuzzy support vector machine (FSVM) is proposed. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the extended DST. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in extended DST domain, and the FSVM model is obtained by training. Then the extended DST detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by FSVM training model. Finally, the detail subbands of extended DST coefficients are denoised by using the adaptive Bayesian threshold. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.
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References
Vijaya Arjunan, R., Vijaya Kumar, V.: Survey analysis of various image denoising techniques—a perspective view. In: Proceedings of the International Conference on VLSI, Communication and Instrumentation, Kottayam, India, pp. 704–708 (2011)
Chatterjee, P., Milanfa, P.: Patch-based near-optimal image denoising. IEEE Trans. Image Process. 21(4), 1635–1649 (2012)
Liu, C., Szeliski, R., Kang, S.B.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 299–3142 (2008)
Brox, T., Kleinschmidt, O., Cremers, D.: Efficient nonlocal means for denoising of textural patterns. IEEE Trans. Image Process. 17(7), 1057–1092 (2008)
Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 290–530 (2005)
Pardo, A.: Analysis of non-local image denoising methods. Pattern Recognit. Lett. 32(16), 2145–2149 (2011)
Van, D.V.D., Kocher, M.: SURE-based non-local means. IEEE Signal Process. Lett. 16(11), 973–976 (2009)
Coupe, P., Manjón, J.V., Robles, M.: Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising. IET Image Process. 6(5), 558–568 (2012)
Van, D.V.D., Kocher, M.: Nonlocal means with dimensionality reduction and SURE-based parameter selection. IEEE Trans. Image Process. 20(9), 2683–2690 (2011)
Rehman, A., Wang, Z.: SSIM-based non-local means image denoising. In: The 18th IEEE International Conference on Image Processing (ICIP), pp. 217–220 (2011)
Salmon, J., Strozecki, Y.: Patch reprojections for non-local methods. Signal Process. 92(2), 477–489 (2012)
Ji, Z., Chena, Q., Suna, Q.-S., et al.: A moment-based nonlocal-means algorithm for image denoising. Inf. Process. Lett. 109(23–24), 1238–1244 (2009)
Dabov, K., Foi, A., Katkovnik, V., et al.: Image denoising by sparse 3d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Salmon, J.: On two parameters for denoising with non-local means. IEEE Signal Process. Lett. 17(3), 269–272 (2010)
Katkovnik, V., Foi, A., Egiazarian, K., Astola, J.: From local kernel to nonlocal multiple-model image denoising. Int. J. Comput. Vis. 86(1), 1–32 (2010)
Roth, S., Black, M.J.: Steerable random fields. In: The 11th International Conference on Computer Vision (ICCV 2007), pp. 1–8 (2007)
Cao, Y., Luo, Y., Yang, S.: Image denoising based on hierarchical Markov random field. Pattern Recognit. Lett. 32(2), 368–374 (2011)
Chen, S., Liu, M., Zhang, W.: Edge preserving image denoising with a closed form solution. Pattern Recognit. 46(3), 976–988 (2013)
Gao, Q., Roth, S.: How well do filter-based MRFs model natural images. In: Pattern Recognition, pp. 62–72. Springer, Berlin (2012)
Ho, J., Hwang, W.L.: Image denoising using wavelet Bayesian network models. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1105–1108 (2012)
Li, Y.P., Huttenlocher, D.P.: Sparse long-range random field and its application to image denoising. In: Proceedings of European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science, vol. 5304, pp. 344–357 (2008)
Barbu, A.: Learning real-time MRF inference for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 1574–1581 (2009)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the 1998 IEEE International Conference on Computer Vision, Washington, DC, pp. 839–846 (1998)
Zhang, M., Gunturk, B.K.: Multiresolution bilateral filtering for image denoising. IEEE Trans. Image Process. 17(12), 2324–2333 (2008)
Liu, G., Ma, W., Ma, Y.: Combination of curvelet threshold with bilateral filtering for image denoising. In: 2012 International Conference on Audio, Language and Image Processing (ICALIP), pp. 469–474 (2012)
Yu, H., Zhao, L., Wang, H.: Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain. IEEE Trans. Image Process. 18(10), 2364–2369 (2009)
Lin, C.H., Tsai, J.S., Chiu, C.T.: Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Trans. Image Process. 19(9), 2307–2320 (2010)
Zhang, K., Lafruit, G., Lauwereins, R.: Constant time joint bilateral filtering using joint integral histograms. IEEE Trans. Image Process. 21(9), 4309–4314 (2012)
Tian, C., Krishnan, S.: Accelerated bilateral filtering with block skipping. IEEE Signal Process. Lett. 20(5), 419 (2013)
Hu, J., Li, S.: Fusion of panchromatic and multispectral images using multiscale dual bilateral filter. In: The 18th IEEE International Conference on Image Processing (ICIP), pp. 1489–1492 (2011)
Lefkimmiatis, S., Maragos, P., Papandreou, G.: Bayesian inference on multiscale models for Poisson intensity estimation: applications to photon-limited image denoising. IEEE Trans. Image Process. 18(8), 1724–1741 (2009)
Fathi, A., Naghsh-Nilchi, A.R.: Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Trans. Image Process. 21(9), 3981–3990 (2012)
Rabbani, H., Gazor, S.: Image denoising employing local mixture models in sparse domains. IET Image Process. 4(5), 413–428 (2010)
Yu, G., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans. Image Process. 21(5), 2481–2499 (2012)
Yin, S., Cao, L., Ling, Y.: Image denoising with anisotropic bivariate shrinkage. Signal Process. 91(8), 2078–2090 (2011)
Chen, G., Zhu, W.P., Xie, W.: Wavelet-based image denoising using three scales of dependency. IET Image Process. 6(6), 756–760 (2012)
Peleg, T., Eldar, Y.C., Elad, M.: Exploiting statistical dependencies in sparse representations for signal recovery. IEEE Trans. Signal Process. 60(5), 2286–2303 (2012)
Dong, W., Wu, X., Shi, G.: Context-based bias removal of statistical models of wavelet coefficients for image denoising. In: The 16th IEEE International Conference on Image Processing (ICIP), pp. 3841–3844 (2009)
Strang, G.: Introduction to Applied Math. Wellesley-Cambridge Press, Wellesley (1986)
Chen, D., MacLachlan, S., Kilmer, M.: Iterative parameter-choice and multigrid methods for anisotropic diffusion denoising. SIAM J. Sci. Comput. 33(5), 2972–2994 (2011)
Liu, F., Liu, J.: Anisotropic diffusion for image denoising based on diffusion tensors. J. Vis. Commun. Image Represent. 23(3), 516–521 (2012)
Tsiotsios, C., Petrou, M.: On the choice of the parameters for anisotropic diffusion in image processing. Pattern Recognit. 46(5), 1369–1381 (2013)
Hajiaboli, M.R.: An anisotropic fourth-order diffusion filter for image noise removal. Int. J. Comput. Vis. 92(2), 177–191 (2011)
Qiu, Z., Yang, L., Lu, W.: A new feature-preserving nonlinear anisotropic diffusion for denoising images containing blobs and ridges. Pattern Recognit. Lett. 33(3), 319–330 (2012)
Krissian, K., Aja-Fernández, S.: Noise-driven anisotropic diffusion filtering of MRI. IEEE Trans. Image Process. 18(10), 2265–2274 (2009)
Li, H.C., Fan, P.Z., Khan, M.K.: Context-adaptive anisotropic diffusion for image denoising. Electron. Lett. 48(14), 827–829 (2012)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Wu, D., Peng, D., Tian, J.: Image denoising using local adaptive least squares support vector regression. Geo-Spat. Inf. Sci. 10(3), 196–199 (2007)
Lin, T.C., Yeh, C.T., Liu, M.K.: Application of SVM-based filter using LMS learning algorithm for image denoising. In: Neural Information Processing, Models and Applications. Lecture Notes in Computer Science, vol. 6444, pp. 82–90 (2010)
Zhang, S., Chen, Y.: Image denoising based on wavelet support vector machine. In: 2006 International Conference on Computational Intelligence and Security, pp. 1809–1812 (2006)
Lin, T.C.: Decision-based filter based on SVM and evidence theory for image noise removal. Neural Comput. Appl. 21(4), 695–703 (2012)
Li, M., Yang, J., Su, Z.Y.: Support vector regression based color image restoration in YUV color space. J. Shanghai Jiaotong Univ. 15(1), 31–35 (2010)
Zhang, G.D., Yang, X.H., Xu, H.: Image denoising based on support vector machine. In: 2012 Spring Congress on Engineering and Technology (S-CET), pp. 1–4 (2012)
Lin, C., Wang, S.: Fuzzy support vector machines. IEEE Trans. Neural Netw. 13(2), 464–471 (2002)
Wang, Y., Wang, S., Lai, K.K.: A new fuzzy support vector machine to evaluate credit risk. IEEE Trans. Fuzzy Syst. 13(6), 820–831 (2005)
Lim, W.Q.: The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans. Image Process. 19(5), 1166–1180 (2010)
Firouzmanesh, A., Boulanger, P.: Image de-blurring using shearlets. In: The Ninth Conference on Computer and Robot Vision (CRV), pp. 167–173 (2012)
Balster, E.J., Zheng, Y.F., Ewing, R.L.: Feature-based wavelet shrinkage algorithm for image denoising. IEEE Trans. Image Process. 14(12), 2024–2039 (2005)
Chen, Y., Ji, Z., Hua, C.: Spatial adaptive Bayesian wavelet threshold exploiting scale and space consistency. Multidimens. Syst. Signal Process. 19(1), 157–170 (2008)
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61272416, 60873222, & 60773031, 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, and Liaoning Research Project for Institutions of Higher Education of China under Grant No. L2013407.
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Wang, XY., Liu, YC. & Yang, HY. An Efficient Remote Sensing Image Denoising Method in Extended Discrete Shearlet Domain. J Math Imaging Vis 49, 434–453 (2014). https://doi.org/10.1007/s10851-013-0476-x
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DOI: https://doi.org/10.1007/s10851-013-0476-x