Abstract
Abstract:
Image restoration algorithms are used to reconstruct
the information that is suppressed when an observed image is
subjected to blurring. These algorithms generally assume that
knowledge of the nature of the distortion and noise contained in
an observed image is available. When this information is not
available and has to be directly estimated from the image being
processed the problem becomes one of blind deconvolution. This
paper makes use of a novel blur identification technique and a
noise identification technique to perform blind deconvolution on
single images that have been degraded by a Gaussian blur and
contain additive white Gaussian noise.