Immediate solution of EM algorithm for non-blind image deconvolution

IF 0.5 Q4 STATISTICS & PROBABILITY
Seung-Gu Kim
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引用次数: 1

Abstract

Due to the uniquely slow convergence speed of the EM algorithm, it su ff ers form a lot of processing time until the desired deconvolution image is obtained when the image is large. To cope with the problem, in this paper, an immediate solution of the EM algorithm is provided under the Gaussian image model. It is derived by finding the recurrent formular of the EM algorithm and then substituting the results repeatedly. In this paper, two types of immediate soultion of image deconboution by EM algorithm are provided, and both methods have been shown to work well. It is expected that it free the processing time of image deconvolution because it no longer requires an iterative process. Based on this, we can find the statistical properties of the restored image at specific iterates. We demonstrate the e ff ectiveness of the proposed method through a simple experiment, and discuss future concerns.
非盲图像反卷积的EM算法即时解
由于EM算法特有的收敛速度较慢的特点,在图像较大的情况下,需要花费大量的处理时间,直到得到所需的反卷积图像。为了解决这一问题,本文给出了高斯图像模型下EM算法的一种即时解。它是通过求出EM算法的递推公式,然后将结果反复代入得到的。本文给出了两种用EM算法直接求解图像去分割的方法,两种方法都有很好的效果。由于它不再需要迭代过程,因此可以节省图像反卷积的处理时间。在此基础上,我们可以找到在特定迭代时恢复图像的统计特性。我们通过一个简单的实验证明了所提出方法的有效性,并讨论了未来需要关注的问题。
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来源期刊
CiteScore
0.90
自引率
0.00%
发文量
49
期刊介绍: Communications for Statistical Applications and Methods (Commun. Stat. Appl. Methods, CSAM) is an official journal of the Korean Statistical Society and Korean International Statistical Society. It is an international and Open Access journal dedicated to publishing peer-reviewed, high quality and innovative statistical research. CSAM publishes articles on applied and methodological research in the areas of statistics and probability. It features rapid publication and broad coverage of statistical applications and methods. It welcomes papers on novel applications of statistical methodology in the areas including medicine (pharmaceutical, biotechnology, medical device), business, management, economics, ecology, education, computing, engineering, operational research, biology, sociology and earth science, but papers from other areas are also considered.
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