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COMP_SCI 331: Introduction to Computational Photography

Quarter Offered

Summer : 9-10:30 MW ; Schiffers

Prerequisites

150 or 211 or 230 or instructor by permission

Description

This course is the first in a two-part series that explores the emerging new field of Computational Photography. Computational photography combines ideas in computer vision, computer graphics, and image processing to overcome limitations in image quality such as resolution, dynamic range, and defocus/motion blur. This course will first cover the fundamentals of image sensing and modern cameras. We will then use this as a basis to explore recent topics in computational photography such as motion/defocus deblurring cameras, light field cameras, and computational illumination.

This course will consist of several homework assignments implemented in Python using the Jupyter Notebook framework. There will be no midterm or final exam. 

  • This course fulfills the Interfaces Breadth & Project Course requirement. 

COURSE COORDINATOR: Prof. Oliver Cossairt

COURSE INSTRUCTOR: Professor Cossairt & Professor Willomitzer (Fall), Florian Schiffers (Summer)

HOMEWORK ASSIGNMENTS:  Homework assignments will consist of implementing several computational photography algorithms in Python using the Jupyter Notebook framework.