The paper present a neuroevolutionary method of monochrome and color images enhancement. The proposed method
is based on local-adaptive approach to image processing. Neural network is tuned to perform enhancement of particular
image using genetic algorithm with use of the generalized image evaluation criterion that relies on the contrast degree
of the processed image.
Getting clear high detailed images with high contrast is an important task in many spheres of science and engineering.
However, it's not always possible because of imperfection of devices or environment conditions. This all leaded to
development of different methods of image enhancement. In this article a developed two-phase full-color image
enhancement algorithm is described.
During the first phase the picture is denoised. Wavelet transformation has been chosen to perform it, because it allows
easily remove high-frequency parts. Also, noise components, especially big random surges of signal, could be presented
like set of local features of signals. Noise can be reduced by thresholding this features.
During the second phase brightness and contrast are automatically tuned up using evolutionary algorithm. Evolutionary
algorithms, which are effective methods of multidimensional optimization, allow quick selection of optimal values of
transformation parameters, using objective optimization criterion.
In paper is created a three-dimensional model of electromagnetic wave scattering on the stratified random discrete
media, including the semitransparent object and inhomogeneous flow of scatterers. There are investigated the
dependencies of scattering and absorbing signals energies from the parameters, describing of inhomogeneous structure
of random discrete media and the flow of scatterers. The results of analysis of frequency spectrum of signal scattered
from the flow are presented.
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