EECS 556: Image Processing
Professor Jeff Fessler
This course covers the fundamentals of imaging and image processing. Topics will include image formation, sampling, interpolation, representation, enhancement, restoration, analysis, and compression.
This course uses an engaged learning format where students read the course material outside of class and class time is spent on team problem solving, inquiry-based learning, and interactive activities.
In lieu of a final exam, students will work in small groups on image processing projects that apply the tools learned in the course as well as using ideas from the contemporary literature.
None required. Detailed lecture notes are provided. Several references on reserve at library.
- 2D continuous-space signals & systems (216 in 2D)
- 2D Fourier transforms
- Optical imaging basics (elementary Fourier optics and lenses)
- 2D sampling
- 2D discrete-space signals & systems (351 in 2D) 2D DSFT
- Filter design
- 2D DFT / 2D FFT / 2D DCT
Non-statistical image processing
- 2D interpolation applications: image resizing, rotation, image registration
- Basic image analysis: edge detection / corner detection
- Basic image enhancement: contrast enhancement / image sharpening / image denoising
Statistical image processing
- Random process image models WSS (wide-sense stationary): image denoising / Wiener filter MRF (Markov random fields): image segmentation
- Image restoration (deblurring)
- Sparsity models: image denoising, image deblurring, image compression
- Image coding / quantization / compression