Course CSE116 · Year I · Summer 2026-2027

Digital Image Processing

Compulsory course in Computer Science (in English), taught by Daniela Maria Cristea.

Overview

Lecturer
Daniela Maria Cristea
Seminar tutor
Manuella Kadar
Type of course
Compulsory
Language of instruction
English
Erasmus language
English
Domain
Computer Science
Field of study
Computer Science (in English)
Form of education
Full-time
Form of instruction
Class / Seminary
Credit awarded by
Grade
Teaching methods
Examples, problems, practical applications

Aims

Training skills in creating applications that require image processing.

Knowledge of algorithms, formulas, methods for transforming an image, starting from the desired result.

Developing skills to perform calculations in a certain color space (obviously including the necessary coordinate transformations from one space to another)

Description of the contribution to the development of a digital portfolio.

Acquiring basic knowledge and skills in digital image processing

Formation of a programming style for processing images of various types (black/ white, grayscale, color, stereo).

Producing ideas for capitalizing on the digital products

Course contents

Introduction to Python. Installation. Image Processing. How do you run a Python program? (4 hours)

Representation of digital images, a simple transformation of an image, encoding/decoding. (4 hours)

Improving images. Implementing specific operations. (4 hours)

Implementing morphological transformations of color images (4 hours)

Conversions and calculations in various color spaces (4 hours)

Image compression and decompression. (4 hours)

Project Development and Verification. (4 hours)

Learning outcomes

The student must demonstrate an understanding of the fundamental concepts of digital image processing and be able to develop
image processing programs in Python, using variables, control structures, functions and basic data structures (lists, strings). They must also
be able to interpret simple requirements, develop elementary algorithms and write syntactically and logically correct code, with a minimum
degree of autonomy.

Assessment

Obtaining a minimum grade of 5 both in the final evaluation and in the evaluation of the portfolio of works and the project 50% final

Recommended reading

Digital image processing
Gonzalez, RC, & Woods, RE
Pearson Education., -, 2018 · -
Digital art (4th ed.)
Paul, C.
Thames & Hudson, -, 2023 · -
Advances in Computer Vision
MIT OpenCourseWare
Massachusetts Institute of Technology, Massachusetts Institute of Technology, 2025 · -
A Python package for data handling and evaluation in deep learning-based medical image analysis
Jungo A and Scheidegger O
Comp. Methods Prog. Biomed, -, 2021 · -
Image Operators: Image Processing in Python
Kinser JM
CRC Press, Boca Raton, FL, 2014 · -
Hands-On Image Processing with Python: Expert Techniques for Advanced Image Analysis and Effectiveness
Dey S
Packt Publishing, Birmingham, 2018 · -
Teaching image processing in engineering using Python
López AFJ
IEEE Rev. Iberoam Tech. Apprentice, -, 2016 · -