Journal of Applied Sciences ›› 2012, Vol. 30 ›› Issue (3): 275-280.doi: 10.3969/j.issn.0255-8297.2012.03.010

• Signal and Information Processing • Previous Articles     Next Articles

Gaussian Noise Reduction with Tetrolet and Active Random Field Model

WANG Rui-ling, SHI Jun   

  1. School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China
  • Received:2011-03-17 Revised:2011-04-30 Online:2012-05-30 Published:2012-05-30

Abstract:

In this study, an image denoising algorithm is proposed by combining Tetrolet transform and active random field (ARF). An image with Gaussian noise is decomposed with Haar wavelets, and the ARF algorithm is used to reduce Gaussian noise in the wavelet domain. After inverse wavelet transform, the Tetrolet transform is used for further denoising. The proposed method is compared with other denoising algorithms including wavelet algorithm, Tetrolet algorithm, Markov random field based algorithm and ARF based algorithm. Experimental results indicate that the proposed approach can effectively reduce Gaussian noise at various levels, and achieve better results than other algorithms.

Key words: Tetrolet transform, active random field model, image denoising, Gaussian noise

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