Robust Noise Estimation Based on Noise Injection
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.
Keywordsnoise estimation mode wavelet transform noise injection
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- 1.Gonzalez, R.C., Woods, R.E.: Image restoration and reconstruction, 3rd edn. Digital Image Processing, Pearson Education, Inc., New Jersey (2008)Google Scholar
- 2.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)Google Scholar
- 4.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)Google Scholar
- 6.Amer, A., Dubois, E.: Fast and Reliable Structure-Oriented Video Noise Estimation. IEEE Transactions on Circuits and Systems for Video Technology 15(1) (2005)Google Scholar
- 7.Förstner, W.: Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images. Lecture Notes on Earth Sciences. Springer (1998)Google Scholar
- 8.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)Google Scholar
- 10.Zlokolica, V., Pižurica, A., Philips, W.: Noise Estimation for Video Processing Based on Spatio-Temporal Gradients. IEEE Signal Processing Letters 13(6) (2006)Google Scholar
- 14.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)Google Scholar
- 15.Kodak: Kodak Lossless True Color Image Suite, http://r0k.us/graphics/kodak/