Abstract
Noise estimation is an important premise for image denoising and the related research therefore has drawn increasing attention and interest. Recent studies show that the distribution mode of local variances in natural image can be used as a simple yet efficacious estimator of the additive noise variance, no matter what distribution the noise follows. However, this type of method has the limitation that the target image must have a sufficiently large area with low pixel value variations. Furthermore, this type of noise estimator almost always lead to overestimation without taking into account the mode of local variance distribution of the noise-free image in textural regions. To improve the accuracy of distribution-mode analysis type of noise estimation and to resolve the problem of overestimation, we propose a novel algorithm using a cascade of wavelet sub-band estimation and noise-injection based rectification. The proposed algorithm reduces the detrimental influence of textural image area, and therefore alleviating overestimation of the noise variance. Extensive experiments and comparative study show the reliability and superiority the proposed method over some existing competitors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gonzalez, R.C., Woods, R.E.: Image restoration and reconstruction, 3rd edn. Digital Image Processing, Pearson Education, Inc., New Jersey (2008)
Rank, K., Lendl, M., Unbehauen, R.: Estimation of image noise variance. In: IEE Proceedings on Vision, Image and Signal Processing, vol. 146, pp. 80–84 (1999)
Immerkær, J.: Fast Noise Variance Estimation. Computer Vision and Image Understanding 64(2), 300–302 (1996)
Tai, S.C., Yang, S.M.: 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 (2008)
Corner, B., Narayanan, R., Reichenbach, S.: Noise estimation in remote sensing imagery using data masking. International Journal of Remote Sensing 24(4), 689–702 (2003)
Amer, A., Dubois, E.: Fast and Reliable Structure-Oriented Video Noise Estimation. IEEE Transactions on Circuits and Systems for Video Technology 15(1) (2005)
Förstner, W.: Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images. Lecture Notes on Earth Sciences. Springer (1998)
Stefano, A.D., White, P.R., Collis, W.B.: Training Methods for Image Noise Level Estimation on Wavelet Components. EURASIP Journal on Applied Signal Processing 16, 2400–2407 (2004)
Donoho, D.L., Johnstone, I.L.: Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of the American Statistical Association 90(12), 1200–1224 (1995)
Zlokolica, V., Pižurica, A., Philips, W.: Noise Estimation for Video Processing Based on Spatio-Temporal Gradients. IEEE Signal Processing Letters 13(6) (2006)
Liu, C., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 299–314 (2008)
Trees, H.L.V.: Detection, Estimation, and Modulation Theory, Part I. John Wiley & Son, New York (2001)
Fernández, S.A., Ferrero, G.V.S., Fernández, M.M., López, C.A.: Automatic Noise Estimation in Images Using Local Statistics. Additive and Multiplicative Cases. Image and Vision Computing 27, 756–770 (2009)
Lukin, V.V., Abramov, S.K., Vozel, B., Uss, M., Chehdi, K.: Performance Analysis 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 (2010)
Kodak: Kodak Lossless True Color Image Suite, http://r0k.us/graphics/kodak/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, C., Yang, X., Zhai, G. (2012). Robust Noise Estimation Based on Noise Injection. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-34778-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34777-1
Online ISBN: 978-3-642-34778-8
eBook Packages: Computer ScienceComputer Science (R0)