A comparison of six deconvolution techniques
A comparison of six deconvolution techniques
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DOI：
10.1007/bf02353672
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发表时间：
1996-06-01
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影响因子：
--
通讯作者：
Bates, RA
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文献类型：
--
作者：
Madden, FN;Godfrey, KR;Bates, RA
我们给出了六种反卷积技术的比较结果。我们考虑的方法基于傅立叶变换、系统辨识、约束优化、三次样条基函数的使用、最大熵和遗传算法。我们通过将这些技术应用于模拟噪声数据来比较它们的性能，以便在单位脉冲响应已知时提取输入函数。模拟数据是通过将已知的脉冲响应与五个不同的输入函数中的每一个进行卷积，然后添加恒定变异系数的噪声来生成的。每个算法都在 500 个数据集上进行了测试，并且我们定义了错误度量以比较不同方法的性能。
We present results for the comparison of six deconvolution techniques. The methods we consider are based on Fourier transforms, system identification, constrained optimization, the use of cubic spline basis functions, maximum entropy, and a genetic algorithm. We compare the performance of these techniques by applying them to simulated noisy data, in order to extract an input function when the unit impulse response is known. The simulated data are generated by convolving the known impulse response with each of five different input functions, and then adding noise of constant coefficient of variation. Each algorithm was tested on 500 data sets, and we define error measures in order to compare the performance of the different methods.