Computational Imaging, Photography and Video

Introduces the latest computational methods in digital imaging that overcome the traditional limitations of a camera and enable novel imaging applications. The course provides a practical guide to topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples.

Prereq: Image Processing
Suggested but not required: Linear Algebra, Digital Signal Processing, Computer Vision

Intended Audience 

Photographers, digital artists, image processing programmers and vision researchers using or building applications for digital cameras or images will learn about camera fundamentals and powerful computational tools, along with many real world examples.

A        Introduction

                Digital photography compared to film photography

                Image formation, Image sensors and Optics


B        Understanding the Camera

                Parameters: Pixel Resolution, Exposure, Aperture, Focus, Color depth, Dynamic range

Nonlinearities: Color response, Bayer pattern, White balance, Frequency response

Noise: Electronic sources

                Time factor: Lag, Motion blur, Iris

Flash settings and operation

Filters: Polarization, Density, Decamired

In camera techniques: Auto gain and white balance, Auto focus techniques, Bracketing


C        Image Processing and Reconstruction Tools 

                Convolution, Overview

Gradient domain operations, Applications in fusion, tone mapping and matting

Graph cuts, Applications in segmentation and mosaicing

Bilateral and Trilateral filters, Applications in image enhancement


D        Improving Performance of Camera

                Dynamic range: Variable exposure imaging and tone mapping, 

Frame rate: High speed imaging using multiple cameras

                Pixel resolution: Super-resolution using jitter

Focus: Synthetic Aperture from camera array for controlled depth of field

E         Image Processing and Reconstruction Techniques

                Brief overview of Computer Vision techniques: Photometric stereo, Depth from defocus, Defogging

                Scene understanding: Depth edges using multiple flashes, Reflectance using retinex

                Denoising using flash and no flash image pairs

Multi-image fusion techniques:

Fusing images taken by varying focus, exposure, view, wavelength, polarization or illumination

Photomontage of time lapse images


Omnidirectional and panoramic imaging


F         Computational Imaging beyond Photography

                Optical tomography, Imaging beyond visible spectrum,

                Coded aperture imaging, multiplex imaging, Wavefront coded Microscopy

Scientific imaging in astronomy, medicine and geophysics


G        Future of Smart and Unconventional Cameras

                Overview of HDR cameras: Spatially adaptive prototypes, Log, Pixim, Smal

                Foveon X3 color imaging

Programmable SIMD camera, Jenoptik, IVP Ranger

Gradient sensing camera

                Demodulating cameras (Sony IDcam, Phoci)

                Future directions


Motivation for the Field
(Also see the symposium page )

Digital photography is evolving rapidly with advances in electronic sensing, processing and storage. The emerging field of computational photography attempts to exploit the cheaper and faster computing to overcome the physical limitations of a camera, such as dynamic range, resolution or depth of field, and extend the possible range of applications. The computational techniques encompass methods from modification of imaging parameters during capture to modern image reconstruction methods from the captured samples.


Many ideas in computational photography are still relatively new to digital artists and programmers although they are familiar with photography and image manipulation techniques. A larger problem is that a multi-disciplinary field that combines ideas from computational methods and modern digital photography involves a steep learning curve. For example photographers are not always familiar with advanced algorithms now emerging to capture high dynamic range images, but image processing researchers face difficulty in understanding the capture and noise issues in digital cameras. These topics, however, can be easily learned without extensive background. The goal of this course is to present both aspects in a compact form.


The new capture methods include sophisticated sensors, electromechanical actuators and on-board processing. Examples include adaptation to sensed scene depth and illumination, taking multiple pictures by varying camera parameters or actively modifying the flash illumination parameters. A class of modern reconstruction methods is emerging. The methods can achieve a ‘photomontage’ by optimally fusing information from multiple images, improve signal to noise ratio and extract scene features such as depth edges. The course briefly reviews fundamental topics in digital imaging and then provides a practical guide to underlying techniques beyond image processing such as gradient domain operations, graph cuts, bilateral filters and optimizations.


The participants learn about topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples. We hope to provide enough fundamentals to satisfy the technical specialist without intimidating the curious graphics researcher interested in photography.

Thanks to the growing prevalence of digital cameras, there has recently been a renewed interest in digital photography-based research and products. The papers at Siggraph conference include high dynamic range, matting, image fusion, synthetic aperture using camera arrays, flash photography and cartooning.  A more detailed list is included in the sample bibliography. I plan to give an overview of these publications and papers at Computer Vision conferences, as well as topics in scientific imaging beyond photography.

Ramesh Raskar

Senior Research Scientist

MERL - Mitsubishi Electric Research Labs

201 Broadway, Cambridge, MA 02139, T 617-621-7533, F 617-621-7550

Email: ,


Ramesh Raskar is a Senior Research Scientist at MERL. His research interests include projector-based graphics, computational photography and non-photorealistic rendering. He has published several articles on imaging and photography including multi-flash photography for depth edge detection, image fusion, gradient-domain imaging and projector-camera systems. His papers have appeared in SIGGRAPH, EuroGraphics, IEEE Visualization, CVPR and many other graphics and vision conferences. He was a course organizer at Siggraph 2002, 2003 and 2004. He is a panel organizer at the Symposium on Computational Photography and Video in Cambridge, MA in May 2005. He is a member of the ACM and IEEE.