| Summary: | 博士 === 國立清華大學 === 電機工程研究所 === 82 === Deconvolution is a signal processing procedure for removing
the effects of a linear signal distorting system to estimate
the desired input signal with only output measurements. The
deconvolution problem can be found in many signal processing
areas such as digital communications, seismic data
processing, speech analysis, image processing, spectroscopy and
ultrasonic nondestructive evaluation. The conventional
predictive deconvolution assumes that the desired signal is a
white process. Generally speaking, the more accurate the model
used for the desired input signal, the better the performance
of the developed deconvolution algorithm is. For instance,
Kormylo and Mendel used a zero-mean white Bernoulli-Gaussian
(B-G) model for the reflectivity sequence in seismology which
is a non-Gaussian sparse spike train with both positive and
negative amplitudes. Quite many B-G model based deconvolution
algorithms were reported in the past 15 years, which provide
deconvolution results with much higher resolution than the
predictive deconvolution does at more computation expense. A
common characteristic of the existing B-G model based
deconvolution algorithms is that they can estimate the desired
input signal with high-resolution and recover the phase of the
(minimum or nonminimum phase) linear signal distorting system.
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