Lecture 1:
Date: Jan. 16
Introduction to computational imaging
Course goals and logistics.
Examples of computational imaging problems that will be studied in the course
Lecture 2:
Date: Jan. 18
Under-determined linear inverse problems
MRI imaging
Sparsity as a key type of structure
Reading materials:
Emmanuel Candès and Michael Wakin, An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), pp. 21 - 30, March 2008 (link)
Mark Davenport, Marco Duarte, Yonina Eldar, and Gitta Kutyniok, Introduction to compressed sensing, (Chapter in Compressed Sensing: Theory and Applications, Cambridge University Press, 2012) (link)
Lecture 3:
Date: Jan. 23
ell-0 vs. ell-1 minimization
Lecture 4:
Date: Jan. 25
Compressed sensing
RIP and impact of noisy measurements
Lecture 5:
Date: Jan. 30
Convex optimization review
Slides on noisy compressed sensing
Slides on convex optimization
Lecture 6:
Date: Jan. 30
Convex optimization review
Slides on convex optimization
Lecture 7:
Date: Feb. 06
Lasso optimization
Lecture 8:
Lectures 9-10:
Date: Feb. 13 & 15
Role of ML in solving inverse problems
Review of deep learning
CNN network structures: Alex-net, VGG, U-net, ResNet, DnCNN
Lecture 11:
Date: Feb 15
Deep learning for inverse problems
Lecture 12:
Date: Feb 27
Generative models for inverse problems
Lecture 13-14:
Dates: Feb 29 and March 5
Diffusion models
Lecture 15:
Date: March 7
Trained and untrained generative models for inverse problems
Lecture 16:
Lecture 17-18:
Date: March 26, 28
Phase retrieval
Compression-based solutions
Lecture 19:
Date: April 2
Snapshot compressive imaging
Lecture 20:
Date: April 4
ADMM
Lecture 21-22:
Date: April 9,11
Coherent imaging and speckle noise
Lecture 22:
Date: April 11
Wave optics