Аннотации:
The paper is devoted to machine learning
methods that focus on texture-type image
enhancements, namely the improvement of
objects in images. The aim of the study is to
develop algorithms for improving images and to
determine the accuracy of the considered models
for improving a given type of images. Although
currently used digital imaging systems usually
provide high-quality images, external factors
or even system limitations can cause images
in many areas of science to be of low quality
and resolution. Therefore, threshold values for
image processing in a certain field of science are
considered.
The first step in image processing is image
enhancement. The issues of signal image
processing remain in the focus of attention of
various specialists. Currently, along with the
development of information technology, the
automatic improvement of images used in any
field of science is one of the urgent problems.
Images were analyzed as objects: state license
plates of cars, faces, sections of the field on
satellite images.
In this work, we propose to use the
models of Super-Resolution Generative
Adversarial Network (SRGAN), Extended
Super-Resolution Generative Adversarial
Networks (ERSGAN). For this, an experiment
was conducted, the purpose of which was to
retrain the trained ESRGAN model with three
different architectures of the convolutional
neural network, i. e. VGG19, MobileNet2V,
ResNet152V2 to add perceptual loss (by pixels),
also add more sharpness to the prediction of the
test image, and compare the performance of
each retrained model. As a result of the study,
the use of convolutional neural networks to
improve the image showed high accuracy, that
is, on average ESRGAN+MobileNETV2 – 91 %,
ESRGAN+VGG19 – 86 %, ESRGAN+
+ResNet152V2 – 96 %
