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Digital Image Processing

 Title: Digital Image Processing Lesson Code: 321-9353 Semester: 8 ECTS: 5 Theory Hours: 3 Lab Hours: 2 Faculty: Karybali Eirini

Content outline

Introduction: what is Digital Image Processing (DIP), fields of using DIP. Digital image fundamentals: elements of visual perception, light and electromagnetic spectrum, image sensing and acquisition, sampling and quantization, mathematical tools used in DIP. Intensity transformation functions. Histogram processing. Spatial filtering, smoothing and sharpening spatial filters. Filtering in the frequency domain: sampling and the Fourier transform of sampled functions, 2-D Discrete Fourier Transform and its properties, filtering in the frequency domain, smoothing and sharpening frequency domain filters. Image restoration: noise models, restoration in the presence of noise only, linear position-invariant degradations, estimating the degradation function, inverse filtering, Minimum Mean Square Error (Wiener) filtering. Image compression: fundamentals (coding, spatial and temporal redundancy, irrelevant information, measuring image information, etc.), basic compression methods (lossy and lossless). Color image processing: color models, pseudocolor and full-color image processing, image segmentation based on color, noise in color images, color image compression.

Learning outcomes

It is the intent of this course that students will:
1. be able to describe and explain basic principles of digital image processing and identify and describe the goal of each stage in a Digital Image Processing System.
2. have a basic understanding of human visual perception.
3. have knowledge of the theoretical background needed for Digital Image Processing.
4. understand digital image representations.
5. be able to use basic relationships between pixels and describe basic transformations.
6. be able to define and compute the histogram of a digital image as well as the information that could be inferred from it.
7. be able to enhance digital images using filtering techniques in the spatial domain.
8. know how to analyze images (as 2-D signals) in the frequency domain through the Fourier transformation.
9. be able to enhance digital images using filtering techniques in the frequency domain.
10. understand the effects of noise on all aspects of digital imaging and implement a range of noise reduction filtering approaches.
11. understand the need for compact image representations, learn the theory of digital image compression and be familiar to the most frequently used compression techniques and the industrial standards that make them useful.
12. be able to describe different color spaces and perform pseudocolor and full-color image processing.
13. be familiar with Matlab programming and Image Processing toolbox.
14. be able to design and implement algorithms that perform image processing.

Prerequisites
Not required.

Basic Textbooks

1. Ψηφιακή Επεξεργασία Εικόνας, 3η έκδοση, Gonzales, Woods, Εκδόσεις Τζιόλα.
2. Ψηφιακή Επεξεργασία Εικόνας, Ιωάννης Πήτας.

1. IEEE Transactions on Image Processing
2. Signal Processing: Image Communication (Elsevier)

Learning Activities and Teaching Methods

Lab exercises, oral examination.

Lectures, επίλυση ασκήσεων με υποδειγματικό τρόπο, Laboratory ασκήσεις.

Lectures 39 hours
Laboratory Exercises 26 hours
Personal study 57 hours
Final exams 3 hours
Course total 125 hours (5 ECTS)

Language of Instruction
Greek, English (for Erasmus students)

Μode of delivery

Face-to-face.

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