Checking date: 06/05/2025 17:12:51


Course: 2025/2026

Digital image processing
(13338)
Bachelor in Sound and Image Engineering (Study Plan 2019) (Plan: 441 - Estudio: 214)


Coordinating teacher: DIAZ DE MARIA, FERNANDO

Department assigned to the subject: Signal and Communications Theory Department

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




Requirements (Subjects that are assumed to be known)
Linear Systems Video System Engineering
Objectives
- Know the mathematical and statistical foundations of Digital Image Processing, emphasizing linear processing. - Know the main sources of image degradation, and the possibilities of image restoration techniques. - Know the basic components of image analysis and image understanding systems. - Know elementary strategies for image segmentation, feature extraction, morphological processing and object recognition. - Know the basic tools for processing image sequences. - Use a specific software environment to implement image processing algorithms. - Implement and use basic processing tools: linear and non-linear local processing, image transform, morphological operators, basic methods for feature extraction and segmentation. - Solve complex image processing problems through the combination of basic processing blocks. - Design strategies and image processing algorithms for solving specific problems.
Learning Outcomes
CB1: Students have demonstrated possession and understanding of knowledge in an area of study that builds on the foundation of general secondary education, and is usually at a level that, while relying on advanced textbooks, also includes some aspects that involve knowledge from the cutting edge of their field of study. CB2: Students are able to apply their knowledge to their work or vocation in a professional manner and possess the competences usually demonstrated through the development and defence of arguments and problem solving within their field of study. ETEGISA1: Ability to construct, develop and manage telecommunication networks, services, processes and applications, such as systems for capture, analog and digital processing, codification, transport, representation, processing, storage, reproduction, audiovisual services presentation and management and multimedia information. ETEGISA2: Ability to analyze, specify, implement and maintain television, audio and video systems, equipment, headends and installations, in fixed as well as mobile environments. RA1: To acquire the knowledge and understanding of the general basic fundamentals of engineering, as well as, in particular, of multimedia communications networks and services, audio and video signal processing, room acoustic control, distributed multimedia systems and interactive multimedia applications specific to Sound and Image Engineering within the telecommunications family. RA2: Be able to carry out an analysis process to solve problems of recording, conditioning, compression of audio and video signals, acoustics of enclosures, networks, services, systems and applications in audiovisual systems. Graduates will be able to identify the problem, recognize the specifications, establish different resolution methods, select the most appropriate one and implement it correctly. They will be able to use various methods and recognize the importance of social constraints, human health, safety, the environment, as well as commercial constraints. RA3: To be competent to carry out engineering designs in their field within Sound and Image Engineering, working as a team. Design encompasses devices, processes, methods and objects, and specifications that are broader than strictly technical, including social awareness, health and safety, environmental and commercial considerations. RA4: To be able to carry out research and carry out innovative contributions in the field of Sound and Image Engineering, including bibliographic search and comprehension as well as the design and development of experiments that solve the challenges of knowledge in the field of audiovisual systems, in terms of the capture, processing, adaptation, diffusion and consumption of multimedia contents, as well as associated networks, services and applications, which justifies the scientific interest of this Degree. RA5: Be competent to apply the knowledge acquired to solve problems and design audiovisual networks and services, to configure their devices, as well as to deploy adaptive, personal audiovisual applications and services on them, bringing network intelligence to the value for the user, maximising the potential of multimedia networks and services in the different social and economic spheres, knowing the environmental, commercial and industrial implications of the practice of engineering in accordance with professional ethics.
Description of contents: programme
0. Introduction. Applications of Digital Image Processing 1. Digital image and Video 2. Basic image processing techniques: pixel-wise transformations, filters, processing in the frequency domain 3. Image restoration 4. Segmentation 5. Morphological image processing 6. Feature extraction and image classification 7. Convolutional Neural Networks (CNNs) and their applications in image processing
Learning activities and methodology
The course will be organized around two types of class sessions: theoretical and laboratory. THEORY The theoretical classes will consist of conventional oral sessions by the teacher, with the aim to present and discuss the fundamental concepts and tools for digital image processing, providing the students with the opportunity to ask and resolve whatever questions arise during learning. Slides, blackboard and software demonstrations will be used to support the session. LAB SESSIONS Between 25% and 40% of the course will be developed in the laboratory, with the following objectives (1) to get some skills in the use of an Image Processing Software, (2) use the image processing tools to visualize the eficacy of the methods discussed during the theoretical sessions, (3) solve simple image processing problems, and (4) complete a final lab project oriented to solve a complex problem.
Assessment System
  • % end-of-term-examination/test 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60

Calendar of Continuous assessment


Extraordinary call: regulations
Basic Bibliography
  • Rafael C. González and Richard E. Woods. Digital Image Processing. Fourth Edition. Pearson. 2018
  • Simon J.D. Prince. Understanding Deep Learning. The MIT Press. 2023
  • Wilhelm Burger and Mark J. Burge. Principles of Digital Image Processing: Fundamental Techniques. Springer-Verlag. 2009
  • Wilhelm Burger and Mark J. Burge. Principles of Digital Image Processing: Core Techniques. Springer-Verlag. 2009
Additional Bibliography
  • Antonio Torralba, Phillip Isola, and William T. Freeman. Foundations of Computer Vision. The MIT Press. 2024
  • Francois Chollet. Deep Learning with Python, Second Edition. Manning. 2021
Recursos electrónicosElectronic Resources *
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The course syllabus may change due academic events or other reasons.