Wiener Filter for Image Noise Reduction (Image Denoising)
There is another Wikipedia entry on Wiener filtering more applicable to image processing.

To summarize (and convert to 2D), given a system:
$$
y(n,m) = h(n,m) * x(n,m)  + v(n,m)
$$
where 


$*$ denotes convolution, 
$x$ is the (unknown) true image,
$h$ is the impulse response of a linear, time-invariant filter,
$v$ is additive unknown noise independent of $x$, and
$y$ is the observed image.


We want to find a deconvolution filter $g$ so that we can estimate $x$ as follows:
$$
\hat{x}(n,m) = g(n,m) * y(n,m)
$$
where $\hat{x}$ is an estimate of $x$ that minimizes the mean square error.

In the frequency domain, the transfer function of $g$, $G$ is:
$$
G(\omega_1, \omega_2) = \frac{H^*(\omega_1, \omega_2)S(\omega_1, \omega_2)}{|H(\omega_1, \omega_2)|^2S(\omega_1, \omega_2) + N(\omega_1, \omega_2)}
$$
where


$G$ is the Fourier transform of $g$,  
$H$ is the Fourier transform of $h$,  
$S$ is the mean power spectral density of the input $x$, and  
$N$ is the mean power spectral density of the noise $v$.


The equation for $G$ can be re-written as:
$$
G(\omega_1, \omega_2) = \frac{1}{H(\omega_1, \omega_2)} 
\left[ \frac{|H(\omega_1, \omega_2)|^2}{H(\omega_1, \omega_2)|^2 + \frac{N(\omega_1, \omega_2)}{S(\omega_1, \omega_2)}}
\right]
$$
So the Wiener filter has the inverse filter for $H$, but also a frequency-dependent term that attenuates the gain based on the signal to noise ratio.