Comparing maximum likelihood estimation and constrained Tikhonov-Miller restoration
Comparing maximum likelihood estimation and constrained Tikhonov-Miller restoration
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DOI：
10.1109/51.482846
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发表时间：
1996-01-01
影响因子：
--
通讯作者：
Strasters, KC
中科院分区：
文献类型：
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作者：
vanKempen, GMP;vanderVoort, HTM;Strasters, KC
作者比较了EM-MLE和ICTM的性能适用于共焦图像。这两种方法大大减少了衍射引起的共焦图像的失真。由于它们的非线性，两者都能够（部分）恢复丢失频率的数据。从作者的实验中可以清楚地看出，对于他们的测试对象，EM-MLE算法的性能远远优于ICTM。EM-MLE算法在作者测试的所有条件下以及作者使用的所有3个性能指标（I-Divergence，MSE，GDT）下都产生了更好的结果。只有在高信噪比条件下，ICTM的MSE性能接近EM-MLE结果。必须指出的是，这一结论仅适用于作者在实验中使用的对象类型（稀疏对象）;对于更密集的对象，情况很可能有所不同。较差的ICTM性能表明，其功能不适合泊松噪声失真的图像。作者在两种算法的结果中均未发现振铃等伪影。圆柱形物体的恢复结果表明，然而，EM-MLE算法有一种倾向，重建的图像是尖锐的，比原来的对象更小。这方面的EM-MLE应彻底调查。Greander的Sieves（1991）方法似乎很有希望用于正则化EM-MLE算法。最后，为了减少ICTM和EM-MLE的计算负担，应该更充分地研究加速这些算法的方法。
The authors have compared the performance of the EM-MLE and ICTM restorations applied to confocal images. Both methods greatly reduce diffraction-induced distortions of confocal images. Due to their nonlinearity, both are able (partially) to restore data of missing frequencies. From the authors' experiments, it is clear that for their test objects, the EM-MLE algorithm performs much better than ICTM. The EM-MLE algorithm produces better results under all the conditions the authors tested, and with respect to all 3 performance measures (I-Divergence, MSE, GDT) the authors used. Only for high SNR conditions, the MSE performance of ICTM approaches the EM-MLE results. It must be noted that this conclusion is only valid for the type of objects the authors used in their experiments (sparse objects); it may well be that for more dense objects, the situation is different. The poor ICTM performance shows that its functional is not well suited for images distorted with Poisson noise. The authors did not find artifacts such as ringing in the results of either algorithm. The restoration results on the cylindrical objects show, however, that the EM-MLE algorithm has a tendency to reconstruct an image that is sharper and smaller than the original object. This aspect of EM-MLE should be investigated thoroughly. Greander's method of Sieves (1991) seems promising for regularizing the EM-MLE algorithm. Finally, to reduce the computational burden of ICTM and EM-MLE, methods to speed up these algorithms should be investigated more fully.