This figure shows restoration of original images from blurred images by applying various deconvolution techniques. Photographs of people's faces on television hidden by little squares can be thought of as examples for degraded images.
Mathematically, a linearly degraded (blurred) image is defined as the convolution of the pristine image with a kernel function with additive noise, i.e.,
where
is the original noise-free, blur-free image,
is the blurred image,
is the kernel function,
is the noise term, and
stands for two-dimensional convolution. The problem is to find a best estimate of
from the noisy blurred data
when
is unknown in many cases.
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