[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.6109/jicce.2011.9.1.059

An Effective Noise Estimator for Use in Noise Reduction

Han, Hag-Yong (Dept. of Electronics Engineering, Dong-A University)
Kwon, Ho-Min (TruDef Research Inc.)
Lee, Sung-Mok (Dept. of Electronics Engineering, Dong-A University)
Lee, Gi-Dong (Dept. of Electronics Engineering, Dong-A University)
Kang, Bong-Soon (Dept. of Electronics Engineering, Dong-A University)

Publication Information

Journal of information and communication convergence engineering / v.9, no.1, 2011 , pp. 59-63 More about this Journal

Abstract

Conventional noise reduction filtering schemes realize limited improvements of the peak signal-to-noise ratio (PSNR) in the low-level noisy images. The flatness degree and the edge information are effectively used to estimate the noise volume. We propose a noise estimator for reducing noise in the AWGN (additive white gaussian noise) corrupted images using three intermediate image maps (FGM(flatness gray map), FIM(flatness index map), NEM(noise estimate map)). The proposed noise estimator is fed into the conventional noise reduction filters as a pre-processor. The performance of noise reduction is tested in the various AWGN corrupted images.

Keywords

noise reduction; noise estimation; low-level noise filtering;

Citations & Related Records

Reference

1	M. Hashemi, S. Beheshti, "Adaptive Noise Variance Estimation in BayesShrink," IEEE Signal Processing Letters, Vol. 17, No.1, January 2010.
2	K. Rank, M. Lendl, and R. Unbehauen, "Estimation of image noise variance," IEE Proc. Vis. Image Signal Processing, Vol. 146, pp. 80-84, Apr. 1999. DOI ScienceOn
3	D.L. Donoho, "De-Noising by Soft-Thresholding," IEEE Transactions on Information Theory, Vol. 41, No. 3, May 1995.
4	S.M. Smith and J.M. Brady, "SUSAN-A New Approach to Low Level Image Processing," International Journal of Computer Vision, Vol. 23, No. 1, pp. 45-78, May 1997. DOI ScienceOn
5	R.C. Bilcu and M. Vehvilainen, "A New Method for Noise Estimation in Images," Proc. IEEE EURASIP International Workshop on Nonlinear Signal and Image Processing, Sapporo, Japan, May 2005.
6	S. I. Olsen, "Estimation of noise in images: An evaluation," Graphical Models and Image Process., Vol. 55, pp. 319-329, July 1993. DOI ScienceOn
7	S.C. Tai and S.M. Yang, "A Fast Method for Image Noise Estimation Using Laplacian Operator and Adaptive Edge Detection," ISCCSP 2008, Malta, March 2008.
8	D.-H. Shin, R.-H Park, S. Yang, and J.-H. Jung, "Block-Based Noise Estimation Using Adaptive Gaussian Filtering," IEEE Trans. on Consumer Electronics, Vol. 51, No. 1, pp. 218-226, 2005. DOI ScienceOn
9	J.S. Lee and K. Hoppel, "Noise modeling and estimation of remotely-sensed image," in Proc. Int. Geoscience and Remote Sensing, Vancouver, Canada,, Vol. 2, pp. 1005-1008, June 1989.
10	R. C. Gonzalez, R. E. Woods and S. L. Eddins, "Digital Image Processing using MATLAB," Pearson Prentice Hall, pp. 89-107, 2004."
11	A. Buades, B. Coll, and J.M. Morel, "A non local algorithm for image denoising," IEEE Computer Vision and Pattern Recognition 2005, Vol. 2, pp. 60-65, 2005.