Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation | ||
| Journal of AI and Data Mining | ||
| مقاله 76، دوره 8، شماره 2، تیر 2020، صفحه 289-301 اصل مقاله (1.76 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2020.7797.1920 | ||
| نویسندگان | ||
| M. Saeedzarandi* ؛ H. Nezamabadi-pour؛ S. Saryazdi | ||
| Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. | ||
| چکیده | ||
| Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wavelet based image denoising, selecting a proper model for wavelet coefficients is very important. In this paper, we model wavelet coefficients in each sub-band by heavy-tail distributions that are from scale mixture of normal distribution family. The parameters of distributions are estimated adaptively to model the correlation between the coefficient amplitudes, so the intra-scale dependency of wavelet coefficients is also considered. The denoising results confirm the effectiveness of the proposed method. | ||
| کلیدواژهها | ||
| Image denoising؛ Wavelet transform؛ MAP estimator؛ Heavy-tail distributions؛ Scale mixture of normal distributions | ||
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آمار تعداد مشاهده مقاله: 728 تعداد دریافت فایل اصل مقاله: 782 |
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