New look at maximum entropy image reconstruction

被引：0
|
作者：
Willis, Matthew [1 ]
Jeffs, Brian D. [1 ]
Long, David G. [1 ]
机构：
[1] Brigham Young Univ, Provo, United States
来源：
Conference Record of the Asilomar Conference on Signals, Systems and Computers | 1999年 / 2卷
关键词：
Algorithms - Approximation theory - Image analysis - Mathematical models - Probability density function;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents new insights into the maximum entropy (ME) method of image restoration. It is shown that when a specific image prior probability pdf model is chosen for Bayesian MAP restoration, the resulting solution is identical to the maximum entropy result. This relationship provides a new means of evaluating the theoretical foundations of maximum entropy, and may assist in determining what class of images are best suited for ME processing. Also, a new non-iterative, closed-form approximation to the ME solution is developed. This result can reduce computational demands compared to conventional iterative algorithms. An example of the closed form restoration is presented.
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页码：1272 / 1276
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