Course Title: Introduction to Digital Image Processing
Type of Course: Optional, Theory
Offered to: EEE
Pre-requisite Course(s): None
With the advent of large-scale computing power, digital image processing has become one of the most rapidly growing fields in electrical engineering. This course is designed to provide an introduction to the basic concepts, methodologies and algorithms of digital image processing. The introductory topics that will be covered in this course include image acquisition, sampling, representation and transformation, image analysis in frequency domain, image enhancement both in spatial and frequency domains, image restoration from degradation process. Some advanced image processing techniques such as image reconstruction from projections, wavelet and multiresolution processing, and image compression will also be studied in this course.
The major goal of the course is to furnish a solid foundation for the students to study advanced topics in image analysis such as computer vision systems, biomedical image analysis, multimedia processing, and artificial intelligence. With the exercise of mathematical formulations both in the theoretical and practical problems, the students will be able to implement advanced algorithms of image analysis and evaluate the performance of image processing algorithms and systems.
Fundamental knowledge of digital signal processing
| COs | CO Statements | Mapping with POs | Learning Domain (Taxonomy Level) |
|---|---|---|---|
| CO1 | Recall the fundamental rules of linear algebra and the concept of sampling and frequency to solve the engineering problems of image processing. | PO(a) | Cognitive (Comprehension) |
| CO2 | Relate the biological system of human vision to interpret the functions of image processing units. | PO(a), PO(b) | Cognitive (Comprehension + Analysis) |
| CO3 | Apply the deterministic and stochastic theories to formulate estimation problems of images in different real-life applications. | PO(a), PO(b) | Cognitive (Comprehension + Analysis + Application) |
| CO4 | Describe the properties of multiresolution analysis and apply the concepts in wavelet-based image processing. | PO(a), PO(b) | Cognitive (Comprehension + Analysis + Application) |
| CO5 | Understand the relation between data and information and employ the idea in formulating image compression algorithms. | PO(a), PO(b) | Cognitive (Comprehension + Analysis + Application) |
| CO6 | Identify real-life applications of image processing and design efficient engineering solution. | PO(a), PO(b), PO(c) | Cognitive (Comprehension + Analysis +Design) |
Cognitive Domain Taxonomy Levels: C1 – Knowledge, C2 – Comprehension, C3 – Application, C4 – Analysis, C5 – Synthesis, C6 – Evaluation, Affective Domain Taxonomy Levels: A1: Receive; A2: Respond; A3: Value (demonstrate); A4: Organize; A5: Characterize; Psychomotor Domain Taxonomy Levels: P1: Perception; P2: Set; P3: Guided Response; P4: Mechanism; P5: Complex Overt Response; P6: Adaptation; P7: Organization
Program Outcomes (PO): PO(a) Engineering Knowledge, PO(b) Problem Analysis, PO(c) Design/development Solution, PO(d) Investigation,
PO(e) Modern tool usage, PO(f) The Engineer and Society, PO(g) Environment and sustainability, PO(h) Ethics, PO(i) Individual work and team work,
PO(j). Communication, PO(k) Project management and finance, PO(l) Life-long Learning
* For details of program outcome (PO) statements, please see the departmental website or course curriculum
| K1 | K2 | K3 | K4 | K5 | K6 | K7 | K8 | P1 | P2 | P3 | P4 | P5 | P6 | P7 | A1 | A2 | A3 | A4 | A5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Topics (According to syllabus in Academic Calendar, 2021) | Lectures (Weeks) | Mapping with COs |
|---|---|---|
| Introduction: Historical perspective, classical and emerging applications, image acquisition, sampling, representation, image processing system | 1-3 (1) |
CO1 |
| Visual Sensors: Human visual system, imaging characteristics of electromagnetic spectrum, imaging sensors, contrast in image, just noticeable difference | 4-6 (2) |
CO1 CO2 |
| Image Transforms: Image intensity transformations, stochastic transformation, deterministic transformation, coordinate transformation, smoothing and sharpening filters, Fuzzy filters, 2D DFT and its properties, filtering in frequency domain, implementation of 2D DFT and 2D IDFT | 7-15 (3-5) |
CO1 CO3 |
| Image Restoration: Linear degradation model for images, noise models and estimation of parameters, denoising, linear position invariant degradation and estimation of degradation function, inverse filtering, image reconstruction from projections | 16-24 (6-8) |
CO1 CO3 |
| Wavelets for Images: History of wavelet, motivation of scale-space theory, subband coding, multiresolutional analysis, 2D DWT, fast implementation of wavelets, wavelet packet analysis, and applications of wavelets in image processing | 25-30 (9-10) |
CO1 CO4 |
| Image Compression: Fundamentals of image compression, compression models and standards, basic compression methods for grayscale and binary images, block transform coding and JPEG, optimal quantization, wavelet-based image compression and JPEG-2000, image watermarking | 31-36 (11-12) |
CO1 CO5 |
| Review | 37-39 (13) | CO6 |
Class Attendance and Participation
Class participation and attendance will be recorded in every class. Participation and attendance for the students may be considered in case the student could not attend the class due to a valid reason (power failure, internet problem, device problem, health problem, etc.). The student has to inform the teacher over email in case of such occurrences. A maximum of three (03) such missed classes can be considered for this course
Quiz, Assignment, Viva and Presentation
Four nos. of tests (Quiz, Assignment, Viva and Presentation) will be taken and best 3 nos. will be counted.
Final Examination
A comprehensive term final examination will be held at the end of the Term following the guideline of Academic Council.
Class Participation 10%
Continuous Assessment 20%
Final Examination 70%
Total 100%
Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing,” Fourth Edition, Parsons Education, 2018
W. K. Pratt, “Digital Image Processing,” Fourth Edition, Wiley Inter-Science, 2007
M. Petrou and P. Bosdogianni, “Image Processing – The Fundamentals,” Second Edition, Wiley Inter-Science, 2010
A. K. Jain, “Fundamentals of Digital Image Processing,” Prentice Hall, 1989