In this course we examine the fundamental digital image processing methods that stem from a signal processing approach.

The students should have a deep understanding of the methods presented in the course as far as both the mathematical analysis and the applications related to each method are concerned.
The students should be able to apply the methods presented or combinations of them, or modifications of them in a real life image processing problem.
Given a real problem, the students should have the experience to decide which method is appropriate to tackle the problem.
The students should be able to do further independent work on the subject including topics which are not covered in the class.

Image Transforms: Definition and Properties of 2-D Unitary Transforms. Singular Value Decomposition. Fourier Transform. Discrete Cosine Transform. Walsh Transform. Hadamard Transform. Karhunen-Loeve Transform. Image Enhancement: Histogram manipulation, noise reduction, Principal Component Analysis.
Image Restoration: Geometric registration. Inverse filtering. Wiener Filtering. Direct Matrix inversion. Non-linear restoration-Simulated annealing. Edge detection and image thresholding. Texture analysis. Wavelets.