inverse filtering

Dividing the degraded spectrum by the PSF's own frequency response to estimate the clean image.

Inverse filtering estimates $\hat F(u,v) = G(u,v)/H(u,v)$ , exact when noise is absent but unstable in general: $\hat F(u,v) = F(u,v) + N(u,v)/H(u,v)$ , so any frequency where $H(u,v)$ is small or zero has its noise term amplified without bound (or left undefined at an exact zero) -- the motivation for Wiener filtering's stabilized alternative.