Аннотации:
Image noise reduction is an important task in the field of computer vision and
image processing. Traditional noise filtering methods may be limited by their
ability to preserve image details. The purpose of this work is to study and
apply deep learning methods to reduce noise in images. The main tasks of
noise reduction in images are the removal of Gaussian noise, salt and pepper
noise, noise of lines and stripes, noise caused by compression, and noise
caused by equipment defects. In this paper, such noises as the removal of
raindrops, dust, and traces of snow on the images were considered. In the
work, complex patterns and high noise density were studied. A deep learning
algorithm, such as the decomposition method with and without preprocessing,
and their effectiveness in applying noise reduction are considered. It is
expected that the results of the study will confirm the effectiveness of deep
learning methods in reducing noise in images. This may lead to the
development of more accurate and versatile image processing methods
capable of preserving details and improving the visual quality of images in
various fields, including medicine, photography, and video
