Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
558054 | Biomedical Signal Processing and Control | 2011 | 6 Pages |
Abstract
This paper investigates the image despeckling issue in the wavelet domain. A maximum a posteriori (MAP) estimation-based image despeckling approach is proposed by incorporating a non-parametric statistical model into a Bayesian inference framework. The proposed non-parametric model formulates the marginal distribution of wavelet coefficients. It 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. Experiments are conducted to demonstrate the superior performance of the proposed approach.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Jing Tian, Li Chen,