Course catalogue

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.

The aim of this course is for students to:

• be able to describe and explain basic principles of digital image processing
• have a basic understanding of human visual perception
• have knowledge of the theoretical background necessary for Digital Image Processing
• understand digital image representations
• be able to use basic relationships between pixels and describe basic transformations
• be able to define and compute the histogram of a digital image and the information that can be extracted from it
• be able to enhance digital images using spatial domain filtering techniques
• know how to analyze images (as 2-D signals) in the frequency domain via the Fourier transform
• be able to enhance digital images using frequency domain filtering techniques
• understand the effect of noise on digital images and perform appropriate filtering
• understand the need for compact image representations, learn the theory of digital image compression and become familiar with the most commonly used compression techniques
• be able to describe different color spaces and perform pseudocolor and full-color image processing
• be familiar with Matlab programming
• be able to design and implement algorithms that perform image processing.

Not required.

Lectures, resolving exercises, laboratory exercises

Laboratory exercises and their examination - 50% of the final grade

Final oral examination - 50% of the final grade

A grade ≥ 5 is required in both of the above

Detailed information regarding the conduct and evaluation of the course can be found in the course e-class (https://eclass.icsd.aegean.gr/courses/ICSD204/) and in the first presentation of the course.

Greek (English for Erasmus students)