Course Overview

This class presents students the opportunity to learn about recent
image processing methods. In particular, students discover the
non-local approaches, discrete modeling and deep neural networks for
image processing problems such as segmentation or image enhancement. 

Learning Achievement

Competence

Course prerequisites

- Level B2 CEFR in English.
- Basic knowledge of image and signal processing.
- Python (or at least Matbal or C).

Grading Philosophy

Practical work.

Course schedule

- Patch-based methods, PatchMatch.
- Generative neural network
- Gradient domain editing
- Application to image editing and computational photography.

Course type

> Integrated class: - 24 hours face-to-face teaching. - 50 hours personal work.

Online Course Requirement

Instructor

Other information

Duration: 6 weeks

Language of instruction: English
Mode of delivery: Face-to-face teaching: personal work

Site for Inquiry