Lecture 1:
Date: Jan. 21
Introduction to computational imaging
Examples of computational imaging problems that will be studied in the course
Course goals and logistics.
Under-determined linear inverse problems
MRI imaging
Sparsity as a key type of structure
Lecture 2:
Date: Jan. 28
ell-0 vs. ell-1 minimization
Compressed sensing
RIP and impact of noisy measurements
Review notes on Fourier transform
Lecture 3:
Date: Feb. 4
Review of convex optimization
Lecture 4:
Date: Feb. 11
Iterative first order methods for convex optimization
Proximal gradient descent
Lecture 5:
Lecture 6:
Date: Feb. 25
Classic denoising methods
Lecture 7:
Date: March 4
Deep learning methods for denoising
Lecture 8:
Date: March 11
Diffusion models and their application in solving inverse problems
Lecture 9:
Date: March 25
Phase retrieval
Compression-based approach to solving inverse problems
Lecture 10:
Date: March 31
Snapshot compressive imaging
Lecture 11:
Date: April 10
Compressive coherent imaging