SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
60610MEEOS-CME0437	Image Processing	4	Spring	2	2	3	7

Course Type :	Elective Course IV
Cycle:	Bachelor      TQF-HE:6. Master`s Degree      QF-EHEA:First Cycle      EQF-LLL:6. Master`s Degree
Language of Instruction:	English
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Profesör Dr. ABDURAZZAG ALI A ABURAS
Dersin Öğretim Eleman(lar)ı:	Profesör Dr. ABDURAZZAG ALI A ABURAS
Dersin Kategorisi:	Programme Specific

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

1. To familiarize students with digital image fundamentals and image transformations. 2. To Introduce algorithms for spatial and frequency domain filtering. 3. To teach students featured image processing applications with deep learning practices.

Course Content:

D1) Digital image fundamentals (Image formation model, sampling, and quantization, representing digital images, spatial and intensity resolution, and image interpolations.) 2) Digital image fundamentals (Basic vector and matrix operations, single pixel and neighborhood operations, geometric transformations, image transforms) 3) Intensity Transformations (Fundamentals of intensity transformation, Image negatives, log and gamma transformations, contrast stretching, intensity level, and bit-plane Slicing.) 4) Intensity Transformations (Histogram processing, histogram equalization, histogram matching, local histogram processing, histogram statistics.) 5) Spatial filtering (Fundamentals of spatial filtering, spatial correlation and convolution, separable filter kernels). 6) Spatial filtering (Smoothing (low-pass) box and Gaussian kernels, sharpening (high-pass) filter kernels, image sharpening using first-second order derivatives.) 7) Filtering in the Frequency Domain (Complex numbers, Fourier series, Fourier transform of 1D continuous functions, sampling theorem, and Fourier transform of 1D sampled functions.) 8) Filtering in the Frequency Domain (2D continuous and discrete Fourier transform and its inverse, relationship between spatial and frequency intervals.) 9) Filtering in the Frequency Domain (2D discrete convolution theorem, frequency domain filtering fundamentals.) 10) Filtering in the Frequency Domain (Low-pass, high-pass, band-reject, and band-pass filters in the frequency domain.) 11) Image Restoration and Reconstruction (Noise models and estimating noise parameters, mean, order statistic, and adaptive filters.)

Course Specific Rules

Course Prerequisites: Programming skills (C/C++, VC++, Python, MATLAB) Good math background (Calculus, Linear Algebra, Statistical Methods)

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:

Knowledge (Described as Theoritical and/or Factual Knowledge.)

Skills (Describe as Cognitive and/or Practical Skills.)

1) The student will increase his/her knowledge and learning based each lecture related to the sequence of the given chapters during the semester weekly schedule.     2.1) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.2) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.3) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.4) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.5) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule     2.6) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.7) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.8) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.9) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.10) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.11) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.12) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.13) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.14) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.15) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.16) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.17) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.18) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.     2.19) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Introduction	review the chapter. Materyal	N/A
2)	Basic vector and matrix operations, single pixel and neighbourhood operations, geometric transformations, and image transforms.	review the chapter Materyal	n/a
3)	Common transformations of the digital image Scaling 2-D Rotation (around the origin) Basic 2D transformations 2D Affine Transformations Projective Transformations solving for translation Iterative Closest Points (ICP) Algorithm Object Instance Recognition Keypoint Matching Algorithm	review the chapter. Materyal	n/a
4)	Common transformations of the digital image Scaling 2-D Rotation (around the origin) Basic 2D transformations 2D Affine Transformations Projective Transformations solving for translation Iterative Closest Points (ICP) Algorithm Object Instance Recognition Keypoint Matching Algorithm	continue from the week. 3 Materyal	n/a
5)	Image projection by math's representation. Goals of Image Analysis Digital Image Terminology The Three Stages of Computer Vision/Image Processing Projection: world coordinates  image coordinates Homogeneous coordinates Projection matrix Inserting photographed objects/Text into images Oriented and Translated Camera Orthographic Projection Field of View (Zoom, focal length)	prepare some program codes. Materyal	n/a
6)	continue from the Week. 5	as in week 5 Materyal	n/a
7)	Filtering Hybrid Images Two views/types of filtering box filter. Image filtering operation Practice with linear filters. Sharpening Sobel filter Another filter: blur Better smoothing with Gaussians what was that convolution? Highpass Kernel/filter. Identity Kernel/filter. Sharpen Kernel/filter. Emboss Kernel/filter. Highpass Kernel/filter. 2d convolution Key properties of linear filters Important filter: Gaussian Smoothing with Gaussian filter Separability of the Gaussian filter the filter windows.	prepare some program code examples. Materyal	n/a
8)	as in the week 7	prepare some program code examples. Materyal	n/a
9)	Differentiation and convolution Finite difference filters (Prewitt, Roberts) Effects of noise Derivative theorem of convolution Derivative of Gaussian filter Designing an edge detector Canny edge detector thinning Hysteresis thresholding Effect of  (Gaussian kernel spread/size) Median filter	prepare some program code examples. Materyal	n/a
10)	Frequency Spectra Fourier analysis in images Fourier Transform equations. Fourier Bases Computing the Fourier Transform The Convolution Theorem Properties of Fourier Transforms Filtering in the frequency domain Is convolution invertible?	prepare some program code examples. Materyal	n/a
11)	A Note About Grey Levels What Is Image Enhancement? Spatial & Frequency Domains Image Histograms Normalized histogram Image Contrast Histograms and Contrast	prepare some program code examples. Materyal	n/a
12)	Noise reduction Cross-correlation Mean filtering Sharpening revisited Filters: Thresholding	prepare some program code examples. Materyal	n/a
13)	Sharpening Spatial Filters Sharpening edge by First and second-order derivatives Discrete form of Laplacian Unsharp masking Gradient mask	prepare some program code examples. Materyal	n/a
14)	all chapters	course revision	n/a

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:	Lecture material will be made available on OnlineBeykoz
References:	1) Recommended: Computer Vision: Algorithms and Applications Richard Szelisk, 2010 2) Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data by Sandipan Dey 2025, ISBN: 978-1789343731

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

(The matrix below shows how the course learning outcomes (CLOs) associates with programme learning outcomes (both KPLOs & SPLOs) and, if exist, the level of quantitative contribution to them.)

Relationship Between CLOs & PLOs

(KPLOs and SPLOs are the abbreviations for Key & Sub- Programme Learning Outcomes, respectively. )

CLOs/PLOs	KPLO 1	KPLO 2				KPLO 3										KPLO 4				KPLO 5
	1	1	2	3	4	1	2	3	4	5	6	7	8	9	10	1	2	3	4	1	2	3	4	5	6	7	8	9	10	11	12
CLO1
CLO2
CLO3
CLO4
CLO5
CLO6
CLO7
CLO8
CLO9
CLO10
CLO11
CLO12
CLO13
CLO14
CLO15
CLO16
CLO17
CLO18
CLO19
CLO20

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Uses and applies theoretical and applied sciences in the field of basic science subjects for the solution of computer engineering problems.
	1.1 Has sufficient knowledge in mathematics, science, computer science and computer engineering; use theoretical and applied knowledge in these fields together to solve computer engineering problems
2)	Analyzes computer engineering applications, designs and develops models to meet specific requirements under realistic constraints and conditions. For this purpose, selects and uses appropriate methods, tools and technologies.
	2.1 Identify, define, formulate and solve complex computer engineering problems; for this purpose select and apply appropriate analytical and modeling methods
	2.2 Designs a complex computer and software based system, process, device or product to meet certain requirements under realistic constraints and conditions, including economics, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues; For this purpose, it applies modern design methods.
	2.3 Selects and effectively uses modern techniques and tools and information technologies required for computer science and computer engineering applications.
	2.4 Designs and conducts experiments, collects data, analyzes and interprets the results for the study of computer science and computer engineering problems and research topics.
3)	Owns the competencies required by the constantly developing field of computer engineering and the global competitive environment.
	3.1 Works effectively individually and in multi-disciplinary teams, takes responsibility.
	3.2 Accesses the information and for this purpose searches for resources, uses databases and other information resources.
	3.3 Awares of the necessity of lifelong learning, follows developments in science and technology and renews itself continuously.
	3.4 Communicates effectively in Turkish, both orally and in writing, has at least one foreign language knowledge at the level of European Language Portfolio B1.
	3.5 Manage projects, has conscious of workplace practices, employee health, environmental and work safety; aware of the legal consequences of engineering applications.
	3.6 Has awareness of the universal and social effects of engineering solutions and applications; has awareness of entrepreneurship and innovativeness and knowledgeable about contemporary issues.
	3.7 Has professional and ethical responsibility.
	3.8 Has information about the standards used in computer engineering applications.
	3.9 Has quality consciousness.
	3.10 Has the capacity to work in at least one of the important application areas of computer science and computer engineering.
4)	Applies the theoretical knowledge in business life during a semester.
	4.1 Experiences all processes in business life.
	4.2 Takes part in activities related to the field of education in a business operating in the field.
	4.3 Questions the application with theoretical knowledge.
	4.4 Compiles the knowledge and experience gained in the field.
5)	S/he acquires the competencies that develop by the expectations of business world and the society defined as the institutional outcomes of our university on the advanced level in relation with his/her field.
	5.1 Acquires the analyzing solving the problems and managing the conflicts.
	5.2 Participates as a team member and takes responsibility in the environments that require the solving of the conflicts and acts as a leader when necessary.
	5.3 Has awareness for ethical and social responsivity.
	5.4 By supporting the learnt courses with quantitative and qualitative data; and by using verbal and visual communication means, s/he transfers them to the groups within and outside his/her group in a systematical and effective way.
	5.5 Evaluates the norms and standards present in the works in which s/he takes responsibility in a critical point of view.
	5.6 Develops strategical, innovative and entrepreneurial ideas.
	5.7 Acquires competence of managing the change.
	5.8 Shows development personally and socially with and awareness for lifelong learning.
	5.9 Follows advanced technologies and developments about digital transformation.
	5.10 Has cultural awareness and s/he transfers this to the groups within and outside his/her field.
	5.11 Has awareness about citizenship competency.
	5.12 Acquires communication in a Foreign Language (English) competence defined on the level of at least B1 in European Language Portfolio. (In programs whose medium of instruction is English, on the level of B2/B2+).

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Case Study
Problem Solving
Demonstration
Views
Laboratory
Reading
Homework
Project Preparation
Thesis Preparation
Peer Education
Seminar
Technical Visit
Course Conference
Brain Storming
Questions Answers
Individual and Group Work
Role Playing-Animation-Improvisation
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Presentation
Final Exam
Quiz
Report Evaluation
Homework Evaluation
Oral Exam
Thesis Defense
Jury Evaluation
Practice Exam
Evaluation of Implementation Training in the Workplace
Active Participation in Class
Participation in Discussions

Relationship Between CLOs & Teaching-Learning, Assesment-Evaluation Methods of the Course

(The matrix below shows the teaching-learning and assessment-evaluation methods designated for the course unit in relation to the course learning outcomes.)

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Project	3	% 20.00
Midterms	1	% 30.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	3	42
Laboratory	0	0	0
Application	0	0	0
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	0	0	0
Presentations / Seminar	0	0	0
Project	1	0	0
Homework Assignments	0	0	0
Total Workload of Teaching & Learning Activities	-	-	42
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	2	0	0
Midterms	1	2	2
Semester Final Exam	1	2	2
Total Workload of Assesment & Evaluation Activities	-	-	4
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			46
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			7