Introduction

Computational photography studies how computation enhances the entire photography workflow — capturing, manipulating, and sharing photographs.

Prerequisites

  • Mathematics: Linear algebra, calculus, probability

  • Computing: OpenCV (Python/C++), MATLAB/Octave

  • Camera: Understanding of basic camera operation

Course Topics

Image Processing and Analysis

  • Digital image representation

  • Pixel/point processes

  • Smoothing and filtering methods

  • Feature extraction

  • Assignments: image filtering and feature detection experiments

Cameras, Optics and Sensors

  • Pinhole cameras

  • Optics and lens behavior

  • How cameras work

  • Sensor technology

Image Blending and Merging

  • Sampling and frequencies

  • Image blending techniques

  • Image features

Computational Photography Applications

  • Panoramas

  • High Dynamic Range (HDR)

  • Image editing

Video

  • Video textures

  • Video stabilization

Computational Cameras

  • Light field cameras

  • Multi-view systems

  • Projector-camera systems

Advanced Topics

  • Newer camera technologies

  • Blur / deblur

  • Social / crowd photography

What is Computational Photography?

Computational photography combines computing, digital sensors, modern optics, actuators, and smart lights to extend traditional photography capabilities:

  • Unbounded dynamic range

  • Variable focus, depth of field, resolution

  • Controllable lighting and reflectance

  • Supports and enhances the medium of photography

Dual Photography

Dual photography exploits Helmholtz reciprocity — the principle that light transport between two points is symmetric — to reconstruct the view from a projector’s perspective using photos taken by a camera.

  • Paper

Panoramas

Panoramas are a canonical example of computational photography: multiple overlapping images are stitched together using feature matching and image warping to create a wide field-of-view composite.

Why Study Computational Photography?

The field enables capabilities impossible with traditional cameras:

  • Overcoming physical sensor limitations (dynamic range, resolution)

  • Post-capture refocusing and depth manipulation

  • Computational illumination and relighting

  • Novel image representations and interactions

  • Applications in medicine, security, art, and consumer photography