Open AccessOverview and Comparison of Python Image Processing Tools with Gabor Functions
Author(s) -
F. F. Lazko
Publication year - 2020
Publication title -
l.n. gumilev atyndaġy euraziâ u̇lttyk̦ universitetìnìn̦ habaršysy. matematika, informatika, mehanika seriâsy
Language(s) - English
Resource type - Journals
eISSN - 2663-1326
pISSN - 2616-7182
DOI - 10.32523/2616-7182/2020-132-3-25-30
Subject(s) - python (programming language) , computer science , image processing , implementation , wavelet , software , gabor wavelet , artificial intelligence , computer graphics (images) , multiresolution analysis , computer vision , theoretical computer science , programming language , image (mathematics) , wavelet transform , discrete wavelet transform
With the invention and development of digital photography technology, the number of images obtained for various purposes has dramatically increased. So the need arose for efficient methods of processing, transferring and storing them. It is obvious that the methods of working with images should be scientifically grounded and reflect the peculiarities of human visual perception. One of such methods is systems of Gabor functions, which are a basis in the space ${\mathbb{L}}^{\mathrm{2}}\left(\mathbb{R}\right)$. Their construction is based on the application of the wavelet theory and multiresolution analysis presented in this article. The next step after building the necessary mathematical model of images is its efficient and convenient software implementation. Python is a great tool for doing this. The purpose of this article is to provide an overview and comparison of libraries containing ready-made implementations of these functions, both as simple linear filters and as whole wavelet-bases.