B5534 - COMPUTATIONAL IMAGING

Learning outcomes

At the end of the course the student knows about computational imaging methods and applications with a focus on solving inverse problems in imaging, such as denoising, deconvolution, single-pixel imaging, and others. He can solve some of the previous imaging problems by using both classic optimization algorithms and modern data-driven approaches with convolutional neural networks (CNNs).

Course contents

- Basics on image processing

- Elementary operations on images: Denise, enhancement, super-resolution, segmentation.

- Mathematical tools for image processing: filters, discrete Fourier transform

- Inverse problems in imaging. Ill posedness.

- Statistical approach and regularization.

-Data driven approach: convolutional and generative neural networks in inverse problems in imaging/

- A case study among debtor, super-resolution, segmentation, tomographic image reconstruction.

- Practical lessons using Python and its libraries.

Readings/Bibliography

Charles Bouman, Foundations of computational imaging, SIAM

Teaching methods

Frontal lessons and exercises with one's own laptop

Assessment methods

Delivery and discussion of a project.

Teaching tools

Slides and codes given by the teacher.

Office hours

See the website of Elena Loli Piccolomini

See the website of Davide Evangelista