Lectures

The accompanying material for each lecture is posted here.

Lecture 1: Introduction

Lecture 2: Multidimensional signals & systems

Lecture 3: Sampling and interpolation

Lecture 4: Discrete-domain signals and systems

Lecture 5: Inverse problems & statistical estimation

Lecture 6: Patch-based priors

Lecture 7: Sparsity-based priors

Lecture 8: Structured Sparsity

Lecture 9: Learning image priors

Lecture 10: Rethinking sensing & sampling

Lecture 11: Computational imaging

Additional Resources

• The supplemental material page contains prerequisite topics you should be familiar with.

• Detailed notes will be available for most lectures on the lecture notes page.

• Videos of all the lectures and tutorials are available in this YouTube playlist.