Summary

The students will gain the theoretical knowledge in computational photography, which allows recording and processing a richer visual experience than traditional digital imaging. They will also execute practical group projects to develop their own computational photography application.

Content

Keywords

Computational Photography, Coded Image Sensing, Non-classical image capture, Multi-Image & Sensor Fusion, Mobile Imaging, Machine Learning

Learning Prerequisites

Required courses

• A basic Signal Processing, Image Processing, and/or Computer Vision course.
• Linear Algebra.

Recommended courses

• Introduction to Computer Vision.
• Signal Processing for Communications.
• Machine Learning.

Important concepts to start the course

• Basic signal/image processing.
• Basic computer vision.
• Basic programming (Python, iOS, Android).

Learning Outcomes

• Identify the main components of a computational photography system.
• Contextualise the main trends in computational optics, sensing, processing, and displays.
• Create a computational photography application.
• Design a computational photography solution to solve a particular imaging task.
• Assess / Evaluate hardware and software combinations for their imaging performance.
• Formulate computational photography challenges that still need to be resolved.

Transversal skills

• Evaluate one's own performance in the team, receive and respond appropriately to feedback.
• Continue to work through difficulties or initial failure to find optimal solutions.

Teaching methods

The course consists of 2 hours of lectures per week that will cover the theoretical basics. An additional 2 hours per week are dedicated to a group project designing, developing, and programming a compuational photography application on a mobile plateform (iOS, Android).

Expected student activities

The studens is expected to attend the class and actively participate in the practical group project, which requires coding on either Android or iOS plateform. The student is also required to read the assigned reading material (book chapters, scientific articles).

Assessment methods

The theoretical part will be evaluated with an oral exam at the end of the semester, and the practical part based on the students' group projects

Supervision

Resources

Bibliography

• Selected book chapters
• Course notes (on moodle)
• Links to relevant scientific articles and on-line resources will be given on moodle.

Moodle Link

• https://go.epfl.ch/CS-413