Gallery 6 1-D Signals
  • The top graph is synthetic data formed using basically a random number generator. A known impulse function was applied to the data which produces our degraded data. The third graph down is the deconvolution of the using the known impulse function. The last graph is the restoration of the data using blind deconvolution (impulse function is not known) with application of SeDDaRA. The process restores almost as much information as if the impulse function were known.
  • This is a computer generated soundwave that sweeps a frequency range from 200 to 600 Hz with a level amplitude of 1.32 units.
  • This sound wave was played through the computer speakers and simultaneously recorded by the computer microphone. As you can see, the result is not a very good reproduction of the original.
  • The signal was processed using our one-dimensional SeDDaRA program. Although there is some amplification of noise in the higher frequencies, the signal has improved tremendously. It sounded better too.
  • A second demonstration of cleaning up a soundwave with an unknown frequency filter applied.
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One-Dimensional Singles

Images can be described as two-dimensional signals.  One standard method of image processing is to apply a 1D function to each row, and then apply it to each column.  As SeDDaRA can be applied to 1D signals.

Here we have some examples of application of SeDDaRA to 1D signals.

This plot is from our paper on application to 1D signals.  The top signal is original.  The next one was convolvued with a filter to reduce the resolution.  The third and fourth are SeDDaRA deconvolutions.

This is a signal created by a combination of sine waves.

This is the output wave from a speaker and recorded on a computer.

This is the deconvolution of that signal using SeDDaRA

This is another example having a truth image, a distorted signal, and a reconstructed signal.

We have not explored this space for some time.  If you have an application idea, we would love to hear it.

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  • Gallery 6 1-D Signals

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