CHEN Guangyi, LUO Guangchun, TIAN Ling, et al., “Noise Reduction for Images with Non-uniform Noise Using Adaptive Block Matching 3D Filtering,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1227-1232, 2017, doi: 10.1049/cje.2017.09.031
Citation: CHEN Guangyi, LUO Guangchun, TIAN Ling, et al., “Noise Reduction for Images with Non-uniform Noise Using Adaptive Block Matching 3D Filtering,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1227-1232, 2017, doi: 10.1049/cje.2017.09.031

Noise Reduction for Images with Non-uniform Noise Using Adaptive Block Matching 3D Filtering

doi: 10.1049/cje.2017.09.031
More Information
  • Corresponding author: LUO Guangchun (corresponding author) received the Ph.D. degree in computer science from University of Electronic Science and Technology of China, Chengdu, China, in 2004. He is currently a professor and the Associate Dean of computer science at the UESTC. (Email:gcluo@uestc.edu.cn)
  • Received Date: 2015-06-04
  • Rev Recd Date: 2015-08-12
  • Publish Date: 2017-11-10
  • Noise reduction is a very important topic in image processing. We propose a new method to deal with the case where the noisy image has different noise levels in different regions. The main idea is to segment automatically the noisy image into several sub-images so that each sub-image has approximately the same noise level. We perform Block matching 3D filtering (BM3D) to these subimages in order to obtain denoised sub-images. We then merge sub-images together and enhance the discontinuous regions between the sub-images by performing BM3D again on small image patches. Our experimental results show the effectiveness of this proposed method in terms of Peak signal to noise ratio (PSNR) when compared with the bivariate wavelet shrinkage and the standard BM3D method. In addition to Gaussian white noise, our method performs better than the bivariate wavelet shrinkage and the standard BM3D method even for signal dependent noise.
  • loading
  • D.L. Donoho and I.M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage", Biometrika, Vol.81, No.3, pp.425-455, 1994.
    T.D. Bui and G.Y. Chen, "Translation invariant denoising using multiwavelets", IEEE Transactions on Signal Processing, Vol.46, No.12, pp.3414-3420, 1998.
    G.Y. Chen, W.P. Zhu and W.F. Xie, "Wavelet-based image denoising using three scales of dependency", IET Image Processing, Vol.6, No.6, pp.756-760, 2012.
    D. Cho, T.D. Bui and G.Y. Chen, "Image denoising based on wavelet shrinkage using neighbour and level dependency", International Journal of Wavelets, Multiresolution and Information Processing, Vol.7, No.3, pp.299-311, 2009.
    D. Cho and T.D. Bui, "Multivariate statistical modeling for image denoising using wavelet transforms", Signal Processing:Image Communication, Vol.20, No.1, pp.77-89, 2005.
    K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, "Image denoising by sparse 3D transform-domain collaborative filtering", IEEE Transactions on Image Processing, Vol.16, No.8, pp.2080-2095, 2007.
    A. Fathi and A.R. Naghsh-Nilchi, "Efficient image denoising method based on a new adaptive wavelet packet thresholding function", IEEE Transactions on Image Processing, Vol.21, No.9, pp.3981-3990, 2012.
    P. Chatterjee and P. Milanfar, "Patch-based near-optimal image denoising", IEEE Transactions on Image Processing, Vol.21, No.9, pp.1635-1649, 2012.
    A. Rajwade, A. Rangarajan and A. Banerjee, "Image denoising using the higher order singular value decomposition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.4, pp.849-862, 2013.
    G. Motta, E. Ordentlich, I. Ramirez, G. Seroussi and M.J. Weinberger, "The iDUDE framework for grayscale image denoising", IEEE Transactions on Image Processing, Vol.20, No.1, pp.1-21, 2011.
    M. Miller and N. Kingsburg, "Image denoising using derotated complex wavelet coefficients", IEEE Transactions on Image Processing, Vol.17, No.9, pp.1500-1511, 2008.
    M. Maggioni and A. Foi, "Nonlocal transform-domain denoising of volumetric data with groupwise adaptive variance estimation", SPIE Electronic Imaging 2012, Doi: 10.1117/12.912109.
    A. Danielyan and A. Foi, "Noise variance estimation in nonlocal transform domain", LNLA 2009, Doi: 10.1109/LNLA.2009.5278404.
    L.I. Rudin, S.J. Osher and E. Fatemi, "Nonlinear total variation based noise removal algorithms", Physica D:Nonlinear Phenomena, Vol.60, No.1-4, pp.259-268, 1992.
    A. Buades, B. Coll and J.M. Morel, "A review of image denoising methods, with a new one", Multiscale Modeling and Simulation, Vol.4, No.2, pp.490-530, 2006.
    A. Buades, B. Coll and J.M. Morel, "A nonlocal algorithm for image denoising", Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, Vol.2, pp.60-65, 2005.
    X. Hou, J.W. Li, Y. Lu and Z. Dong, "Improving nonlocal means method via a no-reference image content metric for MRI denoising", Chinese Journal of Electronics, Vol.23, No.4, pp.735-741, 2014.
    N. He and K. Lu, "A non-local feature-preserving strategy for image denoising", Chinese Journal of Electronics, Vol.21, No.4, pp.651-656, 2012.
    X. Zhao, X. Wang and L. Zhang, "Reaction-Diffusion Equation Based Image Denoising Algorithm", Chinese Journal of Electronics, Vol.21, No.3, pp.495-499, 2012.
    J. Bai and X.C. Feng, "Image denoising and decomposition using non-convex functional", Chinese Journal of Electronics, Vol.21, No.1, pp.102-106, 2012.
    W.G. Zhang, S. Wang, F. Liu, X.B. Gao and L.C. Jiao, "Image denoising using bandelets and hidden Markov tree models", Chinese Journal of Electronics, Vol.19, No.4, pp.646-650, 2010.
    L. Sendur and I.W. Selesnick, "Bivariate shrinkage with local variance estimation", IEEE Signal Processing Letters, Vol.9, No.12, pp.438-441, 2002.
    J.B. MacQueen, "Some Methods for classification and Analysis of Multivariate Observations", Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, Vol.1, pp.281-297, 1967.
    N.G. Kingsbury, "Complex wavelets for shift invariant analysis and filtering of signals", Journal of Applied and Computational Harmonic Analysis, Vol.10, No.3, pp.234-253, 2001.
    D.T. Kuan, A.A. Sawchuk, T.C. Strand and P. Chavel, "Adaptive noise smoothing filter for images with signal dependent noise", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.7, No.2, pp.165-177, 1985.
    R. Oktem, K. Egiazarian, V.V. Lukin, N.N. Ponopmarenko and O.V. Tsymbal, "Locally adaptive DCT filtering for signal dependent noise removal", EURASIP Journal on Advances in Signal Priocessing, DOI: 10.1155/2007/42472.
    K. Dabov, A. Foi, and K. Egiazarian, "Video denoising by sparse 3D transform-domain collaborative filtering", Proc. 15th European Signal Processing Conference, EUSIPCO 2007, Poznan, Poland, pp.145-149, 2007.
    H.M. Nguyen, X. Peng, M.N. Do and Z.P. Liang, "Denoising of MR spectroscopic imaging data with low-rank approximations", IEEE Transactions on Biomedical Engineering, Vol.60, No.1, pp.78-89, 2013.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (471) PDF downloads(309) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return