How does scipy.signal.deconvolve work?

1. How do you Deconvolve a signal in Python?
2. What is deconvolution in signal processing?
3. What is the purpose of deconvolution?
4. Why is deconvolution difficult?

How do you Deconvolve a signal in Python?

The deconvolution has n = len(signal) - len(gauss) + 1 points. So in order to let it also reside on the same original array shape we need to expand it by s = (len(signal)-n)/2 on both sides.

What is deconvolution in signal processing?

Deconvolution is the process of filtering a signal to compensate for an undesired convolution. The goal of deconvolution is to recreate the signal as it existed before the convolution took place. This usually requires the characteristics of the convolution (i.e., the impulse or frequency response) to be known.

What is the purpose of deconvolution?

Deconvolution is a computational method that treats the image as an estimate of the true specimen intensity and using an expression for the point spread function performs the mathematical inverse of the imaging process to obtain an improved estimate of the image intensity.

Why is deconvolution difficult?

The main problem with deconvolution through straightforward inverse filtering is that this is extremely sensitive to any deviations from the “optimal” convolved image for that particular PSF, since inverting the PSF is, numerically, not very stable.