DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Instructor: Ben Ochoa
Email: bochoa at ucsd.edu
Office hours: M 8:00 PM-9:00 PM (secondary) and W 8:00 PM-9:00 PM (primary), EBU3B 3234, and at other times by appointment
TA: Karan Santhosh
Email: ksanthosh at ucsd.edu
Office hours: Tu 4:00 PM-6:00 PM, EBU3B B260A, and at other times by appointment
Note: when emailing the instructor or TA with questions about the class, please put "CSE 273" in the subject line.
Class section ID: 135862
Lecture: MW 6:30 PM-7:50 PM, CENTR 222
Computational photography overcomes the limitations of traditional photography using computational techniques from image processing, computer vision, and computer graphics. This course provides a comprehensive introduction to computational photography and the practical techniques used to overcome traditional photography limitations (e.g., image resolution, dynamic range, and defocus and motion blur) and those used to produce images (and more) that are not possible with traditional photography (e.g., computational illumination and novel optical elements such as those used in light field cameras). Upon completion of this course, students will have an understanding of both traditional and computational photography.
Students enrolled in this course are required to complete assignments and a project, including two project presentations. If you would like your presentation slides reviewed (highly recommended) prior to due date, then at least three days prior to your presentation date, send a draft of your slides to the instructor and TA for review. The instructor and TA will provide you with comments to incorporate into your slides. When presenting to the class, follow the presentation guidelines provided by Professor Charles Elkan. After your presentations to the class, you will receive feedback from the instructor and TA.
All projects will follow specific guidelines, including preparation of a project proposal, draft project report, and final project report. The project need not necessarily advance the state of the field. For example, replicating the results of an innovative paper would be a good project. Projects must be closely inspired by one or two specific high quality papers and should have an experimental aspect. Project reports will be evaluated using these grading criteria.
Prerequisites: Linear algebra, calculus, and optimization. Python, C/C++, or other programming experience.
Assignments will be prepared using LaTeX or Markdown. Programming aspects of the assignments will be completed using various programming languages.
Academic Integrity Policy: Integrity of scholarship is essential for an academic community. The University expects that both faculty and students will honor this principle and in so doing protect the validity of University intellectual work. For students, this means that all academic work will be done by the individual to whom it is assigned, without unauthorized aid of any kind.
Collaboration Policy: It is expected that you complete your academic assignments on your own and in your own words and code. The assignments have been developed by the instructor to facilitate your learning and to provide a method for fairly evaluating your knowledge and abilities (not the knowledge and abilities of others). So, to facilitate learning, you are authorized to discuss assignments with others; however, to ensure fair evaluations, you are not authorized to use the answers developed by another, copy the work completed by others in the past or present, or write your academic assignments in collaboration with another person.
If the work you submit is determined to be other than your own, you will be reported to the Academic Integrity Office for violating UCSD's Policy on Integrity of Scholarship. In accordance with the CSE department academic integrity guidelines, students found committing an academic integrity violation will receive an F in the course.
Grading: Course grades will be weighted as follows.
Assignments: 40%
Initial project proposal: 5%
Revised project proposal: 10%
Initial project presentation: 5%
Draft project report: 10%
Final project report: 20%
Final project presentation: 10%
Late Policy: Assignments will have a submission procedure described with the assignment. Assignments submitted late will receive a 15% grade reduction for each 12 hours late (i.e., 30% per day). Assignments will not be accepted 72 hours after the due date. If you require an extension (for personal reasons only) to a due date, you must request one as far in advance as possible. Extensions requested close to or after the due date will only be granted for clear emergencies or clearly unforeseeable circumstances. You are advised to begin working on assignments as soon as they are assigned.
Assignments, and project proposal and report:
Readings and links:
Lecture topics (tentative):
Helpful textbooks:
Computer Vision: Algorithms and Applications, 2nd edition
Richard Szeliski
Springer, 2022
[Amazon]
Multiple View Geometry in Computer Vision, 2nd edition
Richard Hartley and Andrew Zisserman
Cambridge University Press, 2004
[Cambridge Books Online] [Amazon] [Google]
Digital Image Processing, 4th edition
Rafael C. Gonzalez and Richard E. Woods
Pearson, 2018
[Amazon]
Relevant conferences:
Meeting schedule:
| Date | Meeting |
|---|---|
| Apr 3 | Lecture 1 Introduction and overview |
| Apr 5 | Lecture 2 Cameras and image processing |
| Apr 10 | Lecture 3 Image processing |
| Apr 12 | Lecture 4 Burst photography |
| Apr 17 | Lecture 5 Burst photography |
| Apr 19 | Lecture 6 Burst photography, and camera and image motion |
| Apr 24 | Lecture 7 Computational illumination |
| Apr 26 | Lecture 8 Camera arrays and light field photography |
| May 1 | Lecture 9 Computational imaging |
| May 3 | Group forming meeting |
| May 8 | Group meetings with instructor and TA |
| May 10 | Group meetings with instructor and TA |
| May 15 | Group meetings with instructor and TA |
| May 17 | Group meetings with instructor and TA |
| May 22 | Initial project presentations |
| May 24 | Initial project presentations |
| May 29 | No meeting (Memorial Day observance) |
| May 31 | Group meetings with instructor and TA |
| Jun 5 | Group meetings with instructor and TA |
| Jun 7 | Group meetings with instructor and TA |
| Jun 14 | Final project presentations |
Projects:
| Group members | Project |
|---|---|
| Rafferty Chen and Jihu Mun | Spherical Mosaics |
| Ziyang Fu | TensoRF with the SGGX Microflake Distribution |
| Wesley Chang | Towards Real-Time View Synthesis |
| Anthony Quiroga and Lucy Lee | Reconstruction of Compressed Images using Deep Learning |
| Zhouchonghao Wu, Haoruo Zhang, and Tianyue Zhao | Full-Frame Video Stabilization |
| Manoj Kilaru, Ruchitha Reddy, and Shivaank Agarwal | Creating Seamless Camera Action Sequences by Stitching Images Together |
| Yijian Liu and Shih-Han Chan | Synthesizing Dolly Zoom Effect using Multiple Static Images |
| Bhargavi Dameracharla, Devin Garg, and Junqi Ye | Comparing Image Dehazing Techniques |
We are committed to fostering a learning environment for this course that supports a diversity of thoughts, perspectives, and experiences while respecting your identities (including race, ethnicity, heritage, gender, sex, class, sexuality, religion, ability, age, educational background, etc.). Our goal is to create an inclusive learning environment where all students can feel comfortable and thrive. Accordingly, the instructional staff will make a concerted effort to be welcoming and inclusive to the wide range of students in this course. If there is some way we can help you feel more included, please let one of the course staff know (in person, via email/Piazza, or even using an anonymous note).
We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community. Please understand that others' backgrounds, perspectives, and experiences may be different than your own, and help us build an environment where everyone is welcomed and respected.
If you experience any sort of harassment or discrimination, please contact an instructor as soon as possible. If you prefer to speak with someone outside of the course, please contact the Office for the Prevention of Harassment and Discrimination.
We aim to create an environment in which all students can succeed. If you have a disability, please contact the Office for Students with Disabilities (OSD) and discuss appropriate accommodations as soon as possible. We will work to provide you with the accommodations you need, but you must first provide a current Authorization for Accommodation (AFA) letter issued by the OSD. You are required to present your AFA letters to the instructor and to the department's OSD Liaison so that accommodations may be arranged.
If you are experiencing any insecurities related to basic needs (food, housing, financial resources), there are resources available on campus to help, including The Hub and the Triton Food Pantry. Please visit The Hub for more information.
Last update: May 15, 2023