Referring to this topic, I am interested in a deconvolution using Python.
However, unlike the linked topic above, I want to deconvolve a 2D image. The scipy.signal.deconvolve function unfortunately does not support 2D deconvolution.
This amounts to solving the following equation for f, when h is observed, n is the added noise and g is the convolution kernel, and all are 2d arrays:
f * g + n = h
My first question is therefore: How can I perform a 2D deconvolution in Python?
The most obvious option would be, for a known function g, to transform to Fourier space and divide h by g. I have read however that this is merely good for illustration purposes and fairly inaccurate for science purposes.
So, what would be the cleanest, most accurate way of performing the deconvolution?