Coursework

Helpful Background

• Coding Experience (especially Python): All assignments will be in Python. Even though we don't assume prior experience in Python, you should familiarize yourself, for example by working through some online Python tutorials.
• Calculus, Linear Algebra: Basic understanding of matrix algebra (e.g., least squares, orthogonality, SVD) will be helpful (e.g., CSC336, MAT221, MAT223, MAT240).
• Statistical Signal Processing: Familiarity with probability theory and Bayes' theorem (e.g., MAT377).
• Discrete Signal Processing: Basic understanding of discrete Fourier transform, convolution theorem (e.g., ECE362).

Related courses

• CSC2530: Computational Imaging and 3D Sensing
• CSC2305: Numerical Methods for Optimization Problems
• CSC2503: Foundations of Computer Vision
• CSC2516: Neural Networks and Deep Learning
• ECE537: Random Processes
• ECE1505: Convex Optimization
• ECE1512: Digital Image Processing and Applications

Gradescope

All assignments and the final project proposal and report should be submitted on Gradescope using entry code MXDXRM. If you work as a team, make sure to indicate your team member in the submission.

Assignments (50%)

There will be 6 homework assignments (see schedule) in this class. These assignments will contain some theoretical questions and also implementations of techniques that we will discuss in class. Please refer to assignment writeups (available in the Files tab on Quercus) for details. After you finish, submit your code and report on Gradescope.

There are no "late days" for the assignments. If you choose to submit an assignment late, we will accept it for up to 24h after the submission deadline with a 30% penalty (final grade multiplied by 0.7).

Collaboration Policy: Students are permitted to work together on homework assignments in small groups. However, while students can discuss the assignments together, they must compose their solutions individually. Students must not view or copy code of other students or solutions available on the internet. Names of collaborators must be listed on submitted assignments. Submitting plagiarized solutions is an academic offense and can have severe penalties.

Final Project (50%)

The final project grade takes into account your poster presentation (organization of poster, clarify of presentation, ability to answer question), your source code submission (code organization and documentation), and your final project report (appropriate format and length, abstract, introduction, related work, description of your method, quantitative and qualitative evaluation of your method, results, discussion & conclusion, bibliography).

You can work in teams of up to 3 students for the project. Submit only one proposal and final report for each team. The expected amount of work is relative to the number of team members, so if two teams work on a similar project, we'd expect less work from a smaller team. Before you start to work on the proposal or the report, take a look at some of the past project proposals and reports to give you sense for what's expected (see link at the bottom of this page).

The project proposal is a 1-2 page document that should contain the following elements: clear motivation of your idea, a discussion of related work along at least 3 scientific references (i.e., scientific papers not blog articles or websites), an overview of what exactly your project is about and what the final goals are, milestones for your team with a timeline and intermediate goals. Once you send us your proposal, we may ask you to revise it and we will assign a project mentor to your team.

The final project report should look like a short (~6 pages) conference paper. We expect the following sections, which are standard practice for conference papers: abstract, introduction, related work, theory (i.e., your approach), analysis and evaluation, results, discussion and conclusion, references. To make your life easier, we provide an LaTex template that you can use to get started on your report (see schedule for link).

Schedule and Syllabus

Previous Course Offerings

This course is adapted from the Computational Imaging course designed by Gordon Wetzstein and offered at Stanford University (EE367). Below you can find links to pinhole camera photos and course projects from these previous iterations of the course.

Pinhole Camera Gallery

Links to notable pinhole camera photos:

• Winter 2020 (Stanford)
• Winter 2021 (Stanford)

Previous Course Projects

• Winter 2020 (Stanford)
• Winter 2021 (Stanford)
• Winter 2022 (Stanford)
• Fall 2022 (Toronto)

Acknowledgements

The course is adapted from EE367 at Stanford University by Gordon Wetzstein. Some of the materials used in class build on that from other instructors, including Yannis Gkioulekas, Marc Levoy, Fredo Durand, Ramesh Raskar, Shree Nayar, Paul Debevec, Matthew O'Toole and others, as noted in the slides. Feel free to use these slides for academic or research purposes, but please maintain all acknowledgments. This webpage is based on the website for CS231N and EE367 at Stanford University.