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Blind image noise level estimation using texture-based eigenvalue analysis

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Abstract

Blind noisy image estimation is useful in many visual processing systems. The challenge lies in accurately estimating the image noise level without any priori information of the image. To tackle this challenge, an iterative texture-based eigenvalue analysis approach is proposed in this paper. The proposed approach utilizes the eigenvalue analysis to mathematically derive a new noise level estimator based on weak-textured image patches. Furthermore, a new texture strength measure is proposed to adaptively select weak-textured patches from the noisy image. Experimental results are provided to demonstrate that the proposed image noise level estimation approach yields superior accuracy and stability performance to that of conventional noise level estimation approaches, so that to improve the performance of image denoising algorithm.

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References

  1. Aja-Fernández S, Vegas-Sánchez-Ferrero G, Martín-Fernández M, Alberola-López C (2009) Automatic noise estimation in images using local statistics. Additive and multiplicative cases,. Image Vision Computing 27(6):756–770

    Article  Google Scholar 

  2. Amer A, Dubois E (2005) Fast and reliable structure-oriented video noise estimation. IEEE Trans on Circuits and Systems for Video Technology 15(1):113–118

    Article  Google Scholar 

  3. Chen L, Huang X, Tian J, Fu X (2014) Blind noisy image quality evaluation using a deformable ant colony algorithm. Opt Laser Technol 57:265–270

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

    Article  MathSciNet  Google Scholar 

  5. Gai S, Luo L. (2013) Image denoising using normal inverse Gaussian model in quaternion wavelet domain, Multimedia Tools and Applications, pp 1–18, accepted

  6. Huang X, Chen L, Tian J, Zhang X, Fu X (2014) Blind noisy image quality assessment using block homogeneity. Comput Electr Eng 40(3):796–807

    Article  Google Scholar 

  7. Li B, Lin G, Chen Q, Wang H (2013) Image denoising with patch estimation and low patch-rank regularization, Multimedia Tools and Applications, accepted

  8. Liu X, Tanaka M, Okutomi M (2013) Single-image noise level estimation for blind denoising. IEEE Trans Image Process 22(12):5226–5237

    Article  Google Scholar 

  9. Lowe D (1999) Object recognition from local scale-invariant features. In: Proceedings IEEE International Conference on Computer Vision, Kerkyra, Greece, pp 1150–1157

  10. Muresan D, Parks T (2003) Adaptive principal components and image denoising, In: Proceedings International Conference on Image Processing, Barcelona, Spain, pp 101–104

  11. Pyatykh S, Hesser J, Zheng L (2013) Image noise level estimation by principal component analysis. IEEE Trans Image Process 22(2):687–699

    Article  MathSciNet  Google Scholar 

  12. Rank K, Lendl M, Unbehauen R (1999) Estimation of image noise variance. IEE Proceedings-Vision, Image and Signal Processing 146(2):80–84

    Article  Google Scholar 

  13. Shin D-H, Park R-H, Yang S, Jung J-H (2005) Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans Consum Electron 51(1):218–226

    Article  Google Scholar 

  14. Tai S-C, Yang S-M (2008) A fast method for image noise estimation using Laplacian operator and adaptive edge detection, In: International Symposium on Communications, Control and Signal Processing, St. Julians, Malta, pp 1077–1081

  15. Tian J, Chen L (2012) Image noise estimation using a variation-adaptive evolutionary approach. IEEE Signal Proc Lett 19(7):395–398

    Article  Google Scholar 

  16. Tian J, Ma K-K (2010) Stochastic super-resolution image reconstruction. J Vis Commun Image Represent 21:232–244

    Article  Google Scholar 

  17. Tian J, Ma K-K (2011) A survey on super-resolution imaging. SIViP 5(3):329–342

    Article  MathSciNet  Google Scholar 

  18. Tsai DM, Chen MC, Li WC, Chiu WY (2012) A fast regularity measure for surface defect detection. Mach Vis Appl 23(5):869–886

    Article  Google Scholar 

  19. Zeng W, Lu X, Tan X, Tan X (2013) A local structural adaptive partial differential equation for image denoising, Multimedia Tools and Applications, accepted

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61105010, 61375017), Program for Outstanding Young Science and Technology Innovation Teams in Higher Education Institutions of Hubei Province, China (No. T201202).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, 430081, Wuhan, China

    Xiaotong Huang, Li Chen, Jing Tian & Xiaolong Zhang

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, 430081, Wuhan, China

    Xiaotong Huang, Li Chen, Jing Tian & Xiaolong Zhang

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  1. Xiaotong Huang
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  2. Li Chen
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  3. Jing Tian
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  4. Xiaolong Zhang
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Correspondence to Jing Tian.

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Huang, X., Chen, L., Tian, J. et al. Blind image noise level estimation using texture-based eigenvalue analysis. Multimed Tools Appl 75, 2713–2724 (2016). https://doi.org/10.1007/s11042-015-2452-5

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  • DOI: https://doi.org/10.1007/s11042-015-2452-5

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