Introduction to Color Image Processing

This course develops an understanding of the fundamental characteristics of digital systems used in imaging and introduces basic methods and techniques for processing color images. The course covers basic algorithms for image manipulation, characterization, filtering, segmentation, feature extraction and template matching in direct space and Fourier space. The course provides the opportunity for students to explore a range of practical techniques, by developing their own simple processing functions using Matlab.

The course outline includes and not limited to:

• Introduction and Image Processing Fundamentals
• Image acquisition (Sampling and quantization)
• Image models and representation
• Introduction to color imaging.
• Statistical analysis of image signals
• Image Filtering (linear, nonlinear)
• Image enhancement and restoration
  • Denoising, contrast enhancement,
  • Deblurring
• Image Segmentation
  • Gray-level thresholding
  • Edge detection
  • Region based segmentation
• Morphological Image Processing
  • Dilation and Erosion
  • Opening and Closing
• Elements of image compression
• Lossy image compression - basics notions
• Spatial domain based methods
  • Block Truncation Coding
  • Vector Quantization Coding
  • Predictive Coding
  • Laplacian pyramid image coding
• Frequency domain methods
  • DCT based methods
  • Subband coding
  • Wavelet based methods

on completion of this course the student will acquire knowledge which allows her/him to:

• Understand (ie to describe, analyze and reason about) how monochrome digital images are represented, manipulated, encoded and processed, with emphasis on algorithm design, implementation and performance evaluation. methods of capturing and reproducing images in digital systems.
• Understand (ie to describe, analyze and reason about) how color digital images are represented, manipulated, encoded and processed.
• Make appropriate use of mathematical techniques in color imaging. Demonstrate the use of tools such as spreadsheets and specialist maths applications to solve color imaging problems.

• lectures
• Lab work
• Assignments

Compulsory requirements:

To be eligible for the final exam and project hand-in students are expected to deliver and get approved at at least 80% of all the assignments during the semester

If the minimum number of students registered for the course is less than 5, the course may not run during a semester.