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Adaptive Gaussian Noise Image Removal Algorithm Using Filtering-Based Noise Estimation

Tuan-anh NGUYEN,  Hong-son NGUYEN,  Min-cheol HONG

 

(Dept.of Information and Telecommunication, Soongsil University, Seoul 156-743, Korea)

 

Abstract-This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise. The proposed algorithm consists of two stages: estimation and elimination of noise density. To adaptively deal with variety of the noise amount, a noisy input image is firstly filtered by a lowpass filter. Standard deviation of the noise is computed from different images between the noisy input and its filtered image. In addition, a modified Gaussian noise removal filter based on the local statistics such as local weighted mean, local weighted activity and local maximum is used to control the degree of noise suppression. Experiments show the effectiveness of the proposed algorithm.

 

Key words-denoising;local statistics;Gaussian filtering; noise estimation; Gaussian noise

 

Manuscript Number: 1674-8042(2011)03-0230-05

 

doi: 10.3969/j.issn.1674-8042m.2011.03.007

 

References

 

[1] S.I.Olsen, 1993. Noise variance estimation in images: an evaluation, computer vision graphics image processing. Graphic Models and Image Processing, 55(4): 319-323.
[2] J.S.Lee, K.Hoppel, 1989. Noise modeling and estimation of remotely-sensed image.  Int. Geoscience and Remote Sensing, Vancouver, Canada, 2: 1005-1008.
[3] J.S.Lee,  1989. Refined Filtering of image noise using local statistics. Computer Vision, Graphics and Image processing, 15: 380-389.
[4] G.A.Mastin,  1985. Adaptive filters for Digital noise smoothing, an evaluation, Computer Vision, Graphics and Image process., 31: 103-121.
[5] D.H.Shin, R.H.Park, S.J.Yang, et al, 2005. Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans. on Consumer Electronics, 51(1).
[6] K.Rank, M.Lendl, R.Unbehauen, 1999. Estimation of image noise variance.  IEEE Proc. Vision Image Signal Process., 146: 80-84.
[7] H.C.Andrews, B.R.Hunt, 1997. Digital image restoration, Prentice Hall, New York.
[8] G.R.Arce, 2004. Nonlinear signal processing: a statistical approach, John Wiley and Sons Inc..
[9] T.A.Nodes, N.C.Gallagher, 1982. Median filters: some modifications and their properties.  IEEE Trans. Acoustics, Speech and Signal Process., 30(5):  739-746.
[10] J.B.Bednar, T.K.Watt, 1984. Alpha-trimmed means and their relationship to median filter. IEEE Trans. Acoustics, Speech and Signal Process., 32(1): 145-153.
[11] V.Crnojevic, V.Senk, Z.Trpovski, 2004. Advanced impulse detection based on pixel-wise MAD. IEEE Signal Process. Letters, 11(7): 589-592.
[12] I.Aizenberg, C.Butakoff, 2004. Effective impulse detector based on rank-order criteria. IEEE Signal Process. Letters, 11(3):  363-366.
[13] X.Zhang, Y.Xiong, 2009. Impulse noise removal using directional differences based noise detector and adaptive weighted mean filter. IEEE Signal Process. Letters, 16(4): 295-298.
[14] M.Elad, 2002. On the origin of the bilateral filter and ways to improve it. IEEE Trans. Image Process., 11(10):  1141-1151.
[15]Z.Wang, A.C.Bovik, 2002. A universal image quality index. IEEE Signal Processing Letters, 9(3):  81-84.
 

 

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