- Docente: Elena Loli Piccolomini
- Credits: 6
- SSD: MAT/08
- Language: English
- Moduli: Elena Loli Piccolomini (Modulo 1) Davide Evangelista (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Computer Science (cod. 6698)
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.
Teaching methods
Frontal lessons and exercises with one's own laptop
Assessment methods
Delivery and discussion of a project assigned by the teacher at the end of the course. The project will be discussed by means of slides.
Teaching tools
Slides and codes given by the teacher.
Office hours
See the website of Elena Loli Piccolomini
See the website of Davide Evangelista
SDGs
This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.