A study of using stable alpha distributions to denoising images (stable Gaussian distribution for α =2)
Abstract
In this work, we study the stable Alpha distributions, which are a rich class of probability distributions and for their importance entered into denoising images with the Daul-tree complex Wavelet Transform DT_CWT.
We present the wavelet transform method of characteristic functions of stable distributions for α=2 (Gaussian distribution) and moreover the maximum a posteriori estimator(MAP)method applied to estimate the parameters resulting of the Wavelet Transform of the characteristic function.
The function resulting from the wavelet transform , parameters and images were added to the noise which were implemented in MATLAB using the Peak Signal to Noise Ratio(PSNR). The test showed the effectiveness of our method by denoising of the resulting images, and the results of numerical comparisons with Cauchy distribution[9] and Bivariate distribution by Sendur and selesnick [11] and BM3D to the importance of our results in terms of their approach to the results of BM3D.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The authors retain the copyright and grant the right to publish in the magazine for the first time with the transfer of the commercial right to the Tishreen University Journal -Basic Sciences Series
Under a CC BY- NC-SA 04 license that allows others to share the work with of the work's authorship and initial publication in this journal. Authors can use a copy of their articles in their scientific activity, and on their scientific websites, provided that the place of publication is indicted in Tishreen University Journal -Basic Sciences Series . The Readers have the right to send, print and subscribe to the initial version of the article, and the title of Tishreen University Journal -Basic Sciences Series Publisher
journal uses a CC BY-NC-SA license which mean
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- The licensor cannot revoke these freedoms as long as you follow the license terms.
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.