Comment by xmcqdpt2

> As long as you know the blur function exactly, you can divide the final image by the gaussian function in frequency space and get the original image back (modulo rounding errors).

Those rounding errors are very important though. The Gaussian function goes to zero very quickly and dividing by small numbers is not a good idea.

If your deconvolving a noise free version of the original that also doesn't have any saturated pixels (in the black or white direction) then you can get the pretty close to the original back. I don't think this applies here because the OP is taking a picture of a screen that shows the blurred version, so we've got all kind of error sources. I think the OP is right: the camera is subbing in a known picture of the moon.

It would be interesting to see what happens with anisotropic blur for example, or with a picture of the moon with added fake details (words maybe?) and then blurred.