Contents

Content Description:

Spatial signals are ubiquitous. Whether they are 1D signals, as in audio or time-dependent measurement/sensors, whether these are 2D photographs and LIDAR images or whether they are 3D medical volumes, climate models, or computational fluid studies; these signals are everywhere. While many of the techniques we are covering can be explained in 1D (and for didactic reasons we will fall back to 1D a number of times) there is a fundamental difference when we need to create models of processing for more-than-1D signals. While most of "image" processing is really focused on 2D images, it is important that we keep the 3-dimensional nature of the world we live in in mind from day one. Hence, we are presenting a course, which is mostly a signal and image processing course, but with some topics that are necessary to properly deal with 3D images.

Topics covered will include:

• linear time-invariant systems (LTI)
• Fourier transforms
• sampling + convolution
• DFT + FFT
• basic image transformations, some mathematical basics
• image restoration and reconstruction (denoising)
• wavelets and multi-resolution
• 3D scalar data / projection, rendering
• vectors and tensors