Multi-frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models

被引:4
作者
Yang, Afeng [1 ]
Jiang, Xue [2 ]
Li, David Day-Uei [3 ]
机构
[1] Natl Univ Def Technol, Coll Basic Educ, Changsha, Hunan, Peoples R China
[2] Univ Strathclyde, Dept Naval Architecture Ocean & Marine Engn, Glasgow, Lanark, Scotland
[3] Univ Strathclyde, Ctr Biophoton, Glasgow, Lanark, Scotland
基金
中国国家自然科学基金;
关键词
deconvolution; atmospheric turbulence; image restoration; Bayes methods; inference mechanisms; iterative methods; multiframe blind deconvolution; mixed noise model; space object image restoration; Bayesian inference framework; cost function minimisation; iterative recursion; three limited bandwidth constraint; point spread function;
D O I
10.1049/el.2017.4277
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A mixed noise model is proposed and the multi-frame blind deconvolution is used to restore the images of space objects under the Bayesian inference framework. To minimise the cost function, an algorithm based on iterative recursion was proposed. In addition, three limited bandwidth constraints of the point spread functions were imposed into the solution process to avoid converging to local minima. Experimental results show that the proposed algorithm can effectively restore the turbulence degraded images and alleviate the distortion caused by the noise.
引用
收藏
页码:206 / 208
页数:2
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