Research on wavelet-based contourlet transform algorithm for adaptive optics image denoising

被引：21
|
作者：
Li, Dong-ming [1 ]
Zhang, Li-juan [2 ,3 ]
Yang, Jin-hua [3 ]
Su, Wei [3 ]
机构：
[1] Jilin Agr Univ, Sch Informat Technol, Changchun 130118, Jilin, Peoples R China
[2] Changchun Univ Technol, Coll Comp Sci & Engn, Changchun 130012, Jilin, Peoples R China
[3] Changchun Univ Sci & Technol, Changchun 130022, Peoples R China
来源：
OPTIK | 2016年 / 127卷 / 12期
基金：
美国国家科学基金会;
关键词：
Image denoising; Contourlet transform (CT); Wavelet domain; BayesShrink; Adaptive optics (AO);
D O I：
10.1016/j.ijleo.2016.02.042
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
In this paper, we present a wavelet-based contourlet transform (WBCT) method to adaptive optics (AO) image denoising. This method is implemented through combining with BayesShrink theory to estimate the threshold and then improving the adaptive method of selecting threshold, finally obtaining the optimal threshold. The WBCT transform coefficients of different decomposition scales and different direction to select the adaptive optimal threshold to achieve denoising. We evaluate our algorithm using the DWT-NABayesShrink algorithm, DTCWT-BayesShrink algorithm and CbATD algorithm as a benchmark. Using simulated and real observed AO images, we show that our approach with WBCT algorithm exhibits better performance both in peak signal-to-noise ratio (PSNR) and visual quality, which opens up many perspectives for AO image denoising in the astronautics field. (C) 2016 Elsevier GmbH. All rights reserved.
引用
收藏
页码：5029 / 5034
页数：6
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