CSE 273: Computational Photography

Winter 2022


Instructor: Ben Ochoa
Email: bochoa at
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: Mohammad Shafiei Rezvani Nezhad
Email: moshafie at
Office hours: Tu 5:00 PM-6:00 PM and Th 5:00 PM-6:00 PM, EBU3B B240A, 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: 66170
Lecture: MW 6:30 PM-7:50 PM, WLH 2111

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. When presenting to the class, follow the presentation guidelines provided by Professor Charles Elkan. If you would like your slides reviewed (highly recommended) prior to presentation to the class, 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 prior to your presentation in class. Immediately after your presentation, the slides (pdf, one slide per page) must be submitted to the instructional team. 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: 50%
Initial project presentation: 10%
Final project presentation: 10%
Project report: 30%

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
2011, 1st edition [Amazon] [Google]
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

Meeting schedule:

Date Meeting
Jan 3 Lecture 1 Introduction and overview
Jan 5 Lecture 2 Cameras and image processing
Jan 10 Lecture 3 Image processing
Jan 12 Lecture 4 Burst photography
Jan 17 No meeting (Martin Luther King, Jr. Holiday)
Jan 19 Lecture 5 Burst photography
Jan 24 Lecture 6 Burst photography, and camera and image motion
Jan 26 Lecture 7 Computational illumination
Jan 31 Lecture 8 Camera arrays and light field photography
Feb 2 Lecture 9 Computational imaging
Feb 7 Group meetings with instructor and TA
Feb 9 Group meetings with instructor and TA
Feb 14 Group meetings with instructor and TA
Feb 16 Initial project presentations
Feb 21 No meeting (Presidents' Day Holiday)
Feb 23 Group meetings with instructor and TA
Feb 28 Group meetings with instructor and TA
Mar 2 Group meetings with instructor and TA
Mar 7 Group meetings with instructor and TA
Mar 9 Final project presentations


Group members Project
Vishal Vinod and Navya Rathnakara Shetty Adapting to the Dark: Domain Adaptive Low Light RAW Image Enhancement for Smartphone Cameras
Yuka Chu and Zihan Zhang Flash and No-Flash Image Denoising
Zhao Tang and Zhengyuan Yang Depth of Field Blur Through Stereo Disparity Estimation
Isaac Nealey and Jaiyi Hu Spherical Mosaics from Fixed Wide-Angle Cameras
Emily Zhuang and Evan Serrano Simulated Image Blur from Stereoscopic Images

Diversity and Inclusion

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.

Students with Disabilities

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.

Basic Needs/Food Insecurities

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: March 20, 2022