ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

Entropy-based image noise variance estimation

  • 1.

    School of Computer and Information, Hefei University of Technology,Hefei 230009, China

  • 2.

    Science and Technology on Electro-optic Control Labortory, Luoyang 471009, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.04.012
  • Received Date: 09 October 2014
  • Accepted Date: 20 January 2015
  • Rev Recd Date: 20 January 2015
  • Publish Date: 30 April 2015
    Abstract

  • Abstract

    In the de-noising and segmentation algorithm used to deal with the noise image, it is necessary to know the distribution model and the statistical parameters of noise. A novel noise estimation algorithm was thus proposed. First, the combined value of the input noise image variance and local entropy of each image block was calculated. Then all the comprehensive values were arranged in a descending order, and de-noising was calculated using the corresponding standards deviations in that order. Finally, final noise estimates were selected using the image quality evaluation algorithm. The proposed algorithm does not need pre-processing such as complex filtering, wavelet transform, etc., and can obtain the variance of noise by directly processing a series of input image data. It is simple and easy to implement, has high computational efficiency, and enable BM3D and similar de-noising algorithm to denoise adaptively.

    Abstract

    In the de-noising and segmentation algorithm used to deal with the noise image, it is necessary to know the distribution model and the statistical parameters of noise. A novel noise estimation algorithm was thus proposed. First, the combined value of the input noise image variance and local entropy of each image block was calculated. Then all the comprehensive values were arranged in a descending order, and de-noising was calculated using the corresponding standards deviations in that order. Finally, final noise estimates were selected using the image quality evaluation algorithm. The proposed algorithm does not need pre-processing such as complex filtering, wavelet transform, etc., and can obtain the variance of noise by directly processing a series of input image data. It is simple and easy to implement, has high computational efficiency, and enable BM3D and similar de-noising algorithm to denoise adaptively.
    • FullText(HTML)
  • loading
    • References(24)
  • [1]
    Shin, D H, Park R H, Yang S, et al. Block-based noise estimation using adaptive Gaussian filtering[J]. IEEE Transactions on Consumer Electronics, 2005, 51(1): 218-226.
    [2]
    Amer A, MiticheA, DuboiE. Reliable and fast structureoriented video noise estimation[C]// International Conference on Image Processing. Montreal, Canada: IEEE Press, 2002: 840-843.
    [3]
    Rank K, Lendl M,Unbehauen R. Estimation of image noise variance[J]. IEE Proceedings of Vision, Image Signal Process., 1999, 146(2): 80-84.
    [4]
    Yang S M, Tai S C. Fast and reliable image-noise estimation using a hybridapproach[J]. Journal of Electronic Imaging, 2010, 19(3): 033007(1-15).
    [5]
    J.Starck and F. Murtagh, “Automatic noise estimation from the multi resolution support,” Publ. Astron. Soc. Pacific, vol. 110, no. 744, pp.193-199, 1998.
    [6]
    de Stefano A, White P R, Collis W B. Training methods for image noise level estimation on wavelet components[J]. Journal on Applied Signal Processing, 2004, 2004(16): 2400-2407.
    [7]
    Hashemi M, Beheshti S. Adaptive noise variance estimation in bayesshrink[J]. IEEE Signal Processing Letters, 2010, 17(1): 12-15.
    [8]
    Liu C, Szeliski R, Kang S B, et al. Automatic estimation and removal of noise from a single image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 299-314.
    [9]
    Pyatykh S, Hesser J, Zheng L. Image noise level estimation by principal component analysis[J]. IEEE Transactions on Image Processing, 2013, 22(2): 687-699.
    [10]
    Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095.
    [11]
    Ponomarenko N, Lukin V, Zelensky A, et al. TID2008-A database for evaluation of full-reference visual quality assessment metrics[J]. Advances of Modern Radioelectronics, 2009, 10(4): 30-45.
    [12]
    Kong X, Li K, Yang Q, et al. A new image quality metric for image auto-denoising[C]// International Conference on Computer Vision. Sydney, Australia: IEEE Press, 2013: 2888-2895.
    [13]
    Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
    [14]
    Uss M, Vozel B, Lukin, V V, et al. Image informative maps for estimating noise standard deviation and texture parameters[J]. Journal on Advances Signal Processing, 2011, 2011: 806516(1-12).
    [15]
    Danielyan A, Foi A. Noise variance estimation in nonlocal transform domain[C]// International Workshop on Local and Non-Local Approximation in Image Processing. Tuusula, Finnish: IEEE Press, 2009: 41-45.
    [16]
    Smith G. MeasTex image texture database and test suite centre for sensor signal and information processing[R]. University of Queensland, 1998.
    [17]
    Bilcu R, Vehvilainen M. A New method for noise estimation in images[C]// Proceedings of IEEE-EURASIP Nonlinear Signal Image Process. Sapporo, Japan: IEEE Press, 2005: 25.
    [18]
    Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
    [19]
    Aja-Fernández S, Vegas-Sánchez-Ferrero G, Martín-Fernández M, et al. Automatic noise estimation in images using local statistics. Additive and multiplicative cases[J]. Image and Vision Computing, 2009, 27(6): 756-770.
    [20]
    van Zyl Marais I, Steyn W H. Noise estimation algorithms for onboard image quality assessment[C]// International Conference on Space Technology. Athens, Greece: IEEE Press, 2009: 1-4.
    [21]
    Salmeri M, Mencattini A, Rabottino G, et al. Signal-dependent noise characterization for mammographic images denoising[C]// 16th IMEKO TC4 Symposium on Exploring New Frontiers of Instrumentation and Methods for Electronic Measurements. Florence, Italy: IEEE Press, 2008: 1-6.
    [22]
    Schmidt U, Schelten K, Roth H. Bayesian deblurring with integrated noise estimation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE Computer Society, 2011: 2625-2632.
    [23]
    Zoran D, Weiss Y. Scale invariance and noise in natural images[C]// 12th International Conference on Computer Vision. Kyoto, Japan: IEEE Press, 2009: 2209-2216.
    [24]
    Barducci A, Guzzi D, Marcoionni P, et al. Assessing noise amplitude in remotely sensed images using bit-plane and scatter plot approaches[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(8): 2665-2675.
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    Get Citation
    PDF
    XML
    [1]
    Shin, D H, Park R H, Yang S, et al. Block-based noise estimation using adaptive Gaussian filtering[J]. IEEE Transactions on Consumer Electronics, 2005, 51(1): 218-226.
    [2]
    Amer A, MiticheA, DuboiE. Reliable and fast structureoriented video noise estimation[C]// International Conference on Image Processing. Montreal, Canada: IEEE Press, 2002: 840-843.
    [3]
    Rank K, Lendl M,Unbehauen R. Estimation of image noise variance[J]. IEE Proceedings of Vision, Image Signal Process., 1999, 146(2): 80-84.
    [4]
    Yang S M, Tai S C. Fast and reliable image-noise estimation using a hybridapproach[J]. Journal of Electronic Imaging, 2010, 19(3): 033007(1-15).
    [5]
    J.Starck and F. Murtagh, “Automatic noise estimation from the multi resolution support,” Publ. Astron. Soc. Pacific, vol. 110, no. 744, pp.193-199, 1998.
    [6]
    de Stefano A, White P R, Collis W B. Training methods for image noise level estimation on wavelet components[J]. Journal on Applied Signal Processing, 2004, 2004(16): 2400-2407.
    [7]
    Hashemi M, Beheshti S. Adaptive noise variance estimation in bayesshrink[J]. IEEE Signal Processing Letters, 2010, 17(1): 12-15.
    [8]
    Liu C, Szeliski R, Kang S B, et al. Automatic estimation and removal of noise from a single image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(2): 299-314.
    [9]
    Pyatykh S, Hesser J, Zheng L. Image noise level estimation by principal component analysis[J]. IEEE Transactions on Image Processing, 2013, 22(2): 687-699.
    [10]
    Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8): 2080-2095.
    [11]
    Ponomarenko N, Lukin V, Zelensky A, et al. TID2008-A database for evaluation of full-reference visual quality assessment metrics[J]. Advances of Modern Radioelectronics, 2009, 10(4): 30-45.
    [12]
    Kong X, Li K, Yang Q, et al. A new image quality metric for image auto-denoising[C]// International Conference on Computer Vision. Sydney, Australia: IEEE Press, 2013: 2888-2895.
    [13]
    Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
    [14]
    Uss M, Vozel B, Lukin, V V, et al. Image informative maps for estimating noise standard deviation and texture parameters[J]. Journal on Advances Signal Processing, 2011, 2011: 806516(1-12).
    [15]
    Danielyan A, Foi A. Noise variance estimation in nonlocal transform domain[C]// International Workshop on Local and Non-Local Approximation in Image Processing. Tuusula, Finnish: IEEE Press, 2009: 41-45.
    [16]
    Smith G. MeasTex image texture database and test suite centre for sensor signal and information processing[R]. University of Queensland, 1998.
    [17]
    Bilcu R, Vehvilainen M. A New method for noise estimation in images[C]// Proceedings of IEEE-EURASIP Nonlinear Signal Image Process. Sapporo, Japan: IEEE Press, 2005: 25.
    [18]
    Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
    [19]
    Aja-Fernández S, Vegas-Sánchez-Ferrero G, Martín-Fernández M, et al. Automatic noise estimation in images using local statistics. Additive and multiplicative cases[J]. Image and Vision Computing, 2009, 27(6): 756-770.
    [20]
    van Zyl Marais I, Steyn W H. Noise estimation algorithms for onboard image quality assessment[C]// International Conference on Space Technology. Athens, Greece: IEEE Press, 2009: 1-4.
    [21]
    Salmeri M, Mencattini A, Rabottino G, et al. Signal-dependent noise characterization for mammographic images denoising[C]// 16th IMEKO TC4 Symposium on Exploring New Frontiers of Instrumentation and Methods for Electronic Measurements. Florence, Italy: IEEE Press, 2008: 1-6.
    [22]
    Schmidt U, Schelten K, Roth H. Bayesian deblurring with integrated noise estimation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE Computer Society, 2011: 2625-2632.
    [23]
    Zoran D, Weiss Y. Scale invariance and noise in natural images[C]// 12th International Conference on Computer Vision. Kyoto, Japan: IEEE Press, 2009: 2209-2216.
    [24]
    Barducci A, Guzzi D, Marcoionni P, et al. Assessing noise amplitude in remotely sensed images using bit-plane and scatter plot approaches[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(8): 2665-2675.
    Recommended articles
    TrendMD
    Volume 45 Issue 4 page: 337-344
    Cover

    Article Metrics

    Article views (40) PDF downloads(174)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return