利用加权预测的图像迭代盲解卷积

邸男; 付东辉; 王毅楠

利用加权预测的图像迭代盲解卷积

1.
中国科学院长春光学精密机械与物理研究所,吉林长春 130033;
2.
长春轨道客车股份有限公司,吉林长春 130062

基金项目:

国家863高技术研究发展计划资助项目(No.2006AA703405F)

详细信息

中图分类号: TP391.4
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出版历程
- 收稿日期: 2011-07-11
- 修回日期: 2011-08-13
- 刊出日期: 2011-10-25

Iterative blind deconvolution of image with weighted prediction

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
2.
Changchun Railway Vehicles Co., Ltd., Changchun 130062, China

摘要

摘要: 为解决大气湍流造成的图像退化问题,本文鉴于现有的盲解卷积算法收敛性不稳定,计算量大等特点,提出了一种基于加权预测的迭代盲解卷积算法。对目前性能优秀的用迭代实现盲解卷积的L-R算法进行优化,在每次迭代结束后通过加权方法求出预测值,根据预测值计算方向加速算子,从而大大提高算法的收敛速度。实验表明:该算法不仅可对模糊退化图像进行很好的复原,同时与L-R算法相比收敛速度提高约43.8倍,其迭代速度快的特点决定了算法具有较高的工程实用价值。
- 迭代盲解卷积 /
- 加权预测 /
- L-R算法
Abstract: To modify the image degradation caused by atmospheric turbulence, this paper proposes a new algorithm based on the iterative blind deconvolution with weighted prediction to solve the problems on instability convergence and huge complexity from traditional blind deconvolution methods. By optimizing an existing iterative blind deconvolution L-R algorithm, the proposed algorithm uses the weighting to obtain prediced values at the end of every iterative step, it then calculates the acceleration operators according to the predicted values to improve its convergence speed. Experiments show that the algorithm is capable of restoring the turbulence degraded image and the convergence have speeded about 43.8 times as compared with that of L-R algorithm. The algorithm's fast convergence shows its great practical value.
- iterative blind deconvolution /
- weighted prediction /
- L-R algorithm

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参考文献(1)

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