Image Restoration (unblur image) and Blur Recognition
Blur is a form of bandwidth reduction of
an ideal image due to the imperfect image formation process.
It can be caused by relative motion between the camera and
the original scene, or by an optical system that is out of
focus. When aerial photographs are produced for remote sensing
purposes, blur is introduced by atmospheric turbulence, aberrations
in the optical system, and relative motion between the camera
and the ground. Such blurring is not confined to optical
images, for example electron micrographs are corrupted by
spherical aberrations of the electron lenses, and CT scans
suffer from X-ray scatter.
Today there are different techniques available
for solving the restoration (unblur) problem including Fourier domain
techniques, regularization methods, recursive and iterative
A problem is that without a priori knowledge
of at least approximate parameters of the blur (its mathematical
model), these filters show poor results. If an incorrect
blur model is chosen, the image after filtering will be distorted
rather than restored. All of the known filters attempt to
build a model of blur (blur recognition): blurring function and its point spread
function. More complicated filters also try to make some
assumptions about the ideal image and even create some approximation
Today a lot of different algorithms for blur recognition,
identification and recognition, identification of its parameters exist,
for example the maximum likelihood blur estimation or regularization
approach. The common disadvantages of these algorithms are
their computing complexity and relatively high level of misidentification,
especially for noisy images and for the blur parameters estimation
We utilize an original solution of the problem.
Blur Recognition on the Neural Network based on Multi-Valued Neurons.
As previously mentioned, to restore the blurred image (unblur), it
is very important to know the type of blur and parameters
of the corresponding distorting operator.
To identify a blur and its parameters we
analyze specific distortions of the Fourier spectrum amplitude,
which are implied by each particular blur.
To do this, we use a neural network based
on multi-valued neurons (MVN). It is used both for the blur recognition
(identification) of the distorting operator and for the identification
of the corresponding distorting blur operator parameters. The
multi-valued neurons have a lot of wonderful properties.
The main advantages are the high functionality and simplicity
This solution ensures more than 90% successful
blur recognition even for the noisy images. The corresponding
blur parameters can be correctly identified in 85%-90% cases.
After a type of blur and its parameters are
recognised the image can be restored using several kinds
of methods. A high level of blur and blur parameter identification
ensures a good quality of restoration (unblur). All solutions are
implemented in the software system. Currently this system
supports identification and deblurring of Gaussian, Defocus,
1D Motion and Rectangular blurs.
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