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Approx. 4 months

Assumes 6hrs/wk (work at your own pace)

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Course Summary

This class explores how computation impacts the entire workflow of photography, which is traditionally aimed at capturing light from a 3D scene to form a 2D image. A detailed study of the perceptual, technical and computational aspects of forming pictures, and more precisely the capture and depiction of reality on a (mostly 2D) medium of images is undertaken over the entire term. The scientific, perceptual, and artistic principles behind image-making will be emphasized, especially as impacted and changed by computation.

Topics include the relationship between pictorial techniques and the human visual system; intrinsic limitations of 2D representations and their possible compensations; and technical issues involving capturing light to form images. Technical aspects of image capture and rendering, and exploration of how such a medium can be used to its maximum potential, will be examined. New forms of cameras and imaging paradigms will be introduced.

Why Take This Course?

You will undertake a hands-on approach over the entire term using computational techniques, merged with digital imaging processes to produce photographic artifacts. In addition to understanding how various elements of the computational photography pipeline function together to produce novel - and sometimes stunning - results, you will be given ample opportunity to appreciate and critique artifacts produced/curated by your peers.

Prerequisites and Requirements

Students should be familiar with:

  • College-level linear algebra and calculus: Knowledge of matrices, vectors, differentiation and integration, although the focus will be more on understanding and applying mathematical structures - not necessarily deriving your own;
  • Physics: Vectors, optics;
  • Probability theory: Distributions, density functions.

Programming assignments for this course can be completed either using Python-OpenCV (recommended platform) or Matlab/Octave. Working knowledge of either Python or Matlab would thus be required.

See the Technology Requirements for using Udacity.


Please refer to the course schedule for detailed syllabus set to a suggested timeline for Spring 2015, along with assignment due dates and additional materials.

Here is a brief outline of the topics covered:

Module 1 - Introduction

  • Introduction
  • What is Computational Photography
  • Dual Photography
  • Panorama
  • Why Study Computational Photography

Module 2 - Digital Imaging

  • What is a Digital Image
  • Point Processes
  • Smoothing
  • Blending Modes
  • Convolution and Cross-Correlation
  • Gradients
  • Edges

Module 3 - Cameras

  • Cameras
  • Lenses
  • Exposure
  • Sensor

Module 4 - Comp Vision to Comp Photo

  • Fourier Transform
  • Blending
  • Pyramids
  • Cuts
  • Features

Module 5 - Applications

  • Panorama
  • HDR
  • Time Lapse
  • Procam Systems
  • Mosaics

Module 6 - Light Field

  • Lightfield
  • Lightfield Camera

Module 7 - Blur / De-Blur

  • Lucy-Richardon Blur
  • Flutter Shutter

Module 8 - Video

  • Video
  • Video Textures
  • Video Stabilization

Module 9 - Closing Thoughts

Further resources:

Instructors & Partners

instructor photo

Irfan Essa

Irfan Essa is a Professor in the School of Interactive Computing (iC) and Associate Dean in the College of Computing (CoC), at the Georgia Institute of Technology (GA Tech), in Atlanta, Georgia, USA. Professor Essa works in the areas of Computer Vision, Computer Graphics, Computational Perception, Robotics and Computer Animation, Machine Learning, and Social Computing, with potential impact on Video Analysis and Production (e.g., Computational Photography & Video, Image-based Modeling and Rendering, etc.) Human Computer Interaction, Artificial Intelligence, Computational Behavioral/Social Sciences, and Computational Journalism research. He has published over 150 scholarly articles in leading journals and conference venues on these topics and several of his papers have also won best paper awards. He has been awarded the NSF CAREER and was elected to the grade of IEEE Fellow. He has held extended research consulting positions with Disney Research and Google Research and also was an Adjunct Faculty Member at Carnegie Mellon’s Robotics Institute. He joined GA Tech Faculty in 1996 after his earning his MS (1990), Ph.D. (1994), and holding research faculty position at the MIT Media Lab (1988-1996).

instructor photo

David Joyner

David Joyner completed his Ph.D. in Human-Centered Computing at Georgia Tech specializing in delivering automated feedback and assessment to students in exploratory learning environments. He joined Udacity to develop exercises, projects, and (one day!) entire courses that adapt to the learner's ability and progress.

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Arpan Chakraborty

Arpan likes to find computing solutions to everyday problems. He is interested in human-computer interaction, robotics and cognitive science. He obtained his PhD from North Carolina State University, focusing on biologically-inspired computer vision. At Udacity, he spends a good chunk of time designing interactive exercises for his courses, besides working on pet projects to improve or automate workflow.