A wavelet-domain non-parametric statistical approach for image denoising

IEICE Electronics Express

Jing Tian²  Li Chen²  Lihong Ma¹ 

[1] Guangdong Key Lab. of Wireless Network and Terminal, School of Electronic and Information Engineering, South China University of Technology;School of Computer Science and Technology, Wuhan University of Science and Technology

关键词: image denoising;    statistical modeling;    wavelet;   

DOI  :  10.1587/elex.7.1409

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(13)Cited-By(6)The challenge of conventional parametric model-based wavelet image denoising approaches is that the efficiency of these methods greatly depends on the accuracy of the prior distribution used for modeling the wavelet coefficients. To tackle the above challenge, a non-parametric statistical model is proposed in this paper to formulate the marginal distribution of wavelet coefficients. The proposed non-parametric model differs from conventional parametric models in that the proposed model is automatically adapted to the observed image data, rather than imposing an assumption about the distribution of the data. Furthermore, the proposed non-parametric model is incorporated into a Bayesian inference framework to derive a maximum a posterior (MAP) estimation-based image denoising approach. Experiments are conducted to not only demonstrate that the proposed non-parametric statistical model is more suitable than conventional models to formulate the marginal distribution of wavelet coefficients, but also show that the proposed image denoising approach outperforms the conventional approaches.

【 授权许可】

Unknown