Vector Space Methods for Signal and Image Processing, 5 op

Course Content:

• Vector and inner-product spaces
• Bases, Analysis and Synthesis
• Orthonormalization, Approximations
• Continuous-variable models for discrete data
• Global vs Local Processing
• Windowing and weighted norms
• Moving least squares
• Frames and Dual frames
• Singular Value Decomposition, Principal components and KLT transforms
• Smoothing operators
• Discrete differential operators
• Color spaces and color and multispectral image processing
• Popular transforms
• Noise propagation

All the above is explained and demonstrated through a vast set of working examples in Matlab and Python.