Scope of the course: Wavelet transforms for image representation and denoising; Sparsity and signal denoising; Challenges in analysis of multispectral and hyperspectral imagery; Feature extraction and reduction of single-channel and multi-dimensional imagery; Image analysis.
Related topics: Remote sensing flow; Modeling and compensating for atmospheric affects; Spectral distortion & cross-track uniformity; Interpolation of 3-D point-cloud data to gridded data.
Pre-requisites: Digital Signal Processing or an equivalent course (or permission of the instructor), Matlab knowledge.
Class (Spring 2014): Mondays, Wednesdays – 4pm-5.30pm, Room D3 E323
Office Hours: Monday, Wednesday – 3pm – 4pm.
Recommended Reference Books:
- Digital Image Processing by R. C. Gonzalez and R. E. Woods
- Introduction to Wavelets and Wavelet Transforms: A Primer by C. S. Burrus, R. A. Gopinath, H. Guo.
Background: This is an advanced course on image processing – we will be covering advanced algorithms for image representation and analysis. It is expected that students have a basic background in signal processing, matrix theory, probability, linear systems and basic image processing prior to taking this course. To help you refresh some of these basic concepts, I have assembled a list of topics that you should be familiar with.
Review of preliminaries (Links point to presentations and online content reviewing this material):
Review of basic image processing concepts (This material can be found in a basic image processing text-book, such as Gonzalez & Woods, and is covered in a basic graduate level image processing course):
- Introduction to image processing
- Digital image fundamentals
- Image enhancement in the spatial domain
- Image enhancement in the frequency domain