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Image Denoising Based on Nonsubsampled Contourlet Transform and Bivariate Model |
Bian Ce; Zhong Hua; Jiao Li-cheng |
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an 710071, China |
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Abstract This paper proposes a new image denoising method based on the NonsubSampled Contourlet Transform(NSCT) and the bivariate model under the framework of Bayesian MAP estimation theory. The proposed algorithm uses the NSCT’s advantages of translation-invariant and multidirection-selectivity, exploits the intra-scale and inter-scale correlations of NSCT coefficients, and elaborates the method of noise estimation. Compared with some current outstanding denoising methods, the simulation results and analysis show that the proposed algorithm obviously outperforms in both Peak Signal-to-Noise Ratio(PSNR) and visual quality, and effectively preserves detail and texture information of original images.
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Received: 16 October 2007
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