Image processing (126 Information systems and technologies)

Type: For the student's choice

Department: optoelectronics and information technologies




SemesterAmount of hoursLecturerGroup(s)
732Senior Researcher Krupych  O. M.ФеС-42

Laboratory works

SemesterAmount of hoursGroupTeacher(s)
732ФеС-42Senior Researcher Krupych  O. M.

Опис навчальної дисципліни

The purpose of teaching the subject


This course aims to familiarize students with the theoretical foundations of digital image processing and provide them with the necessary practical skills, including the programmatic implementation of basic algorithms. The course provides insights into the history of development and the current state of the subject. The basic concepts, features of digital image processing methods and tools used to improve human visual perception, on the one hand, and to transform images for their storage and transmission via communication channels, as well as to analyze, recognize and interpret images by machine vision devices, including for decision-making and controlling the behavior of complex autonomous technical systems, on the other hand, are considered.


Objectives of the subject


After studying this discipline, the student should




basics of digital image representation, elements of visual perception;
spatial methods of image enhancement, gradational transformations;
frequency methods of image enhancement, low-pass and high-pass filters;
principles of image restoration, methods of noise reduction;
basics of color image processing;
wavelet transform and multiple-scale processing, series decomposition;
methods of image compression, criteria for correct reproduction;
principles of morphological image processing;
methods of image segmentation;
basic principles of pattern recognition theory;


be able to:


analyze the features of image formation using gamma and X-rays, as well as in the ultraviolet, visible, infrared, microwave and radio waves;
read and register images, perform their sampling and quantization;
use frequency filters of images, in particular to improve their quality and clarity, reduce noise;
implement color image processing;
perform wavelet transform and multiple-scale processing;
perform image compression with minimal losses;
implement algorithms for morphological processing and image segmentation;
perform object recognition.






To study this course, you should have knowledge of the following subjects: “Digital Information Processing, Mathematical Statistics and Probability Theory.

Recommended Literature

  1. Digital Image Processing. 2nd Ed. by Rafael C. Fonzalez & Richard E. Woods, Prentice Hall, 2002. – 793p.


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