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Robust Noise Estimation Based on Noise Injection

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Abstract

Noise estimation is an important premise for image denoising and many other image processing applications, and related research has drawn increasing attention and interest. In this paper, a novel noise level estimation algorithm is proposed by investigating the distribution of local variances in natural images. There are two major contributions of this work to tackle with the challenges in noise estimation: 1) a wavelet decomposition based preliminary estimation stage to alleviate the influence of image’s textural or structural information; 2) a noise injection based estimation stage to simulate the impact of noise-free image content on the variance distribution, which otherwise almost always leads to overestimation. The cascade scheme of this two-step estimation procedure can reduce the detrimental influence of textural image regions effectively and therefore relieves overestimation of the noise variance. Moreover, the proposed method is not limited to any specific type of noise distribution. Extensive experiments and comparative analysis demonstrate that the proposed algorithm can reliably infer noise levels and has robust performance over a wide range of visual content, as compared to relevant methods.

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References

  1. Gonzalez, R.C., & Woods, R.E. (2008). Image restoration and reconstruction. Digital image processing, 3rd edition (pp. 313–319). New Jersey: Pearson Education, Inc.

    Google Scholar 

  2. Rank, K., Lendl, M., Unbehauen, R. (1999). Estimation of image noise variance. In IEE proceedings on vision, image and signal processing, (vol. 146, pp. 80–84).

  3. Immerkær, J. (1996). Fast noise variance estimation. Computer Vision and Image Understanding, 64(2), 300–302.

    Article  Google Scholar 

  4. Tai, S.C., & Yang, S.M. (2008). A fast method for image noise estimation using Laplacian operator and adaptive edge detection. In Proceedings of 3rd international symposium on communications, Control and Signal Processing (ISCCSP) (pp. 1077–1081).

  5. Corner, B., Narayanan, R., Reichenbach, S. (2003). Noise estimation in remote sensing imagery using data masking. International Journal of Remote Sensing, 24(4), 689–702.

    Article  Google Scholar 

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

    Article  Google Scholar 

  7. Förstner, W. (1998). Image preprocessing for feature extraction in digital intensity, color and range images. Springer Lecture Notes on Earth Science, 95, 165–189.

    Google Scholar 

  8. Mastin, G.A. (1985). Adaptive filters for digital noise smoothing, an evaluation. Compute Vision, Graphics, and Image Process, 31, 103–121.

    Article  Google Scholar 

  9. Lukin, V.V., Abramov, S.K., Vozel, B., Uss, M., Chehdi, K. (2010). Performance analyst of segmentation-based method for blind evaluation of additive noise in images. In Proceedings of international Kharkov symposium on physics and engineering of microwaves, millimeter and submillimeter waves (MSMW) (pp. 1–3).

  10. Vozel, B., Chehdi, K., Klaine, L. (2006). Noise identification and estimation of its statistical parameters by using unsupervised variational classification. In Proceedings of international conference on acoustics, speech, and signal processing (ICASSP) (Vol. 2, pp. 841–844).

  11. Donoho, D.L., & Johnstone, I.L. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(12), 1200–1224.

    Article  MATH  MathSciNet  Google Scholar 

  12. Stefano, A.D., White, P.R., Collis, W.B. (2004). Training methods for image noise level estimation on wavelet components. EURASIP Journal on Applied Signal Processing, 16, 2400–2407.

    Article  Google Scholar 

  13. Zlokolica, V., Pižurica, A., Philips, W. (2006). Noise estimation for video processing based on spatio-temporal gradients. IEEE Signal Processing Letters, 13(6), 337–340.

    Article  Google Scholar 

  14. Liu, C., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T. (2008). Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 299–314.

    Article  Google Scholar 

  15. Trees, H.L.V. (2001). Detection of signals-estimation of signal parameters. Detection, estimation, and modulation theory, Part I (pp. 246–286). New York: Wiley.

  16. Liu, W., & Lin, W.S. (2012). Gaussian noise level estimation in SVD domain for images. In Proceedings of IEEE international conference on multimedia and expo (ICME) (pp. 830–835).

  17. Fernández, S.A., Ferrero, G.V.S., Fernández, M.M., López, C.A. (2009). Automatic noise estimation in images using local statistics. Additive and multiplicative cases. Image and Vision Computing, 27, 756–770.

    Article  Google Scholar 

  18. Tang, C., Yang, X., Zhai, G. (2012). Robust noise estimation based on noise injection. Lecture Notes in Computer Science, 7674, 142–152.

    Article  Google Scholar 

  19. Kodak. Kodak lossless true color image suite. http://r0k.us/graphics/kodak/.

Download references

Acknowledgments

This paper was supported by National Nature Science Foundation of China (NSFC) (61025005, 60932006, 61001145, 61102098), Science and Technology Commission of Shanghai Municipality (STCSM) (12DZ2272600), Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (20090073110022), China Postdoctoral Science Foundation (CPSF) (20100480603), Shanghai Postdoctoral Science Foundation (11R21414200), 111 Project (B07022).

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Authors and Affiliations

  1. Shanghai Key Labs of Digital Media Processing and Communication, Shanghai Jiao Tong University, Shanghai, China

    Chongwu Tang, Xiaokang Yang & Guangtao Zhai

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  1. Chongwu Tang
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  2. Xiaokang Yang
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  3. Guangtao Zhai
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Correspondence to Chongwu Tang.

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Tang, C., Yang, X. & Zhai, G. Robust Noise Estimation Based on Noise Injection. J Sign Process Syst 74, 69–78 (2014). https://doi.org/10.1007/s11265-013-0745-3

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  • DOI: https://doi.org/10.1007/s11265-013-0745-3

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