In this talk I will give the traditional camera a fresh, "computational" look: I will show that we can significantly boost the optical performance of a camera by slightly changing the way it captures photos: instead of taking a single snapshot at the press of a button, the camera should record a whole sequence of wide-aperture photos, corresponding to a special type of "focal stack." This sequence is then merged algorithmically into a final photo that the photographer sees.
By generalizing the traditional photographic concepts of "depth of field" and "exposure time" to the case of focal stacks, I will show that this type of photography has two performance advantages: (1) we can capture a given depth of field much faster than one-shot photography allows, and (2) we can significantly increase the quality (i.e., signal-to-noise ratio) of photos captured within a restricted exposure time. I will consider these advantages in detail and discuss their implications for photography.
Kyros Kutulakos is a Professor of Computer Science at the University of Toronto. He received a BA degree from the University of Crete and a PhD from the University of Wisconsin, Madison, both in Computer Science. Following his dissertation work, he held faculty appointments at the University of Rochester and the University of Toronto, and a visiting scholar appointment at Microsoft Research Asia.
Prof. Kutulakos is a recipient of an Alfred P. Sloan Research Fellowship, an Ontario Premier's Research Excellence Award, and four best paper prizes (a Best Paper Honorable Mention at the 2006 European Conference on Computer Vision; a David Marr Prize Honorable Mention in 2005; a David Marr Prize in 1999; and an Outstanding Paper Award at the Computer Vision and Pattern Recognition Conference in 1994). He is Program Co-Chair of the upcoming International Conference on Computational Photography and served as Program Co-Chair of the Computer Vision and Pattern Conference in 2003.
