Course

Unit 1

Mathematical Background for Image Processing: Review of Vectors and Matrices – Review of Probability and statistics. Digital Image Fundamentals: Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Basic Relationships between Pixels – Image interpolation. Intensity Transformations and Spatial Filtering: Basic Intensity transformation Functions – Histogram Processing – Fundamentals of Spatial Filtering – Smoothing and Sharpening Spatial Filters.

Unit 2

Filtering in Frequency Domain: 2D Discrete Fourier Transforms – Basics of filtering – Image Smoothing and Image Sharpening Using Frequency Domain Filters – Selective Filtering, Image Restoration: Noise Models – Restoration using Spatial Filters – Periodic Noise Reduction by Frequency Domain Filters.

Unit 3

• Pratt W K, “Digital Image Processing”, Fourth Edition, John Wiley & Sons, 2007.
• Castleman K R, “Digital Image Processing”, Prentice Hall, 1996.
• Gonzalez, Woods and Eddins, “Digital Image Processing Using MATLAB”, Prentice Hall, 2004.
• Russ J C, “The Image Processing Handbook”, CRC Press, 2007