Massachusetts Institute of Technology researchers Sam Hasinoff, Fredo Durand, and William Freeman, along with University of Toronto researcher Kiriakos Kutulakos, have designed a mathematical model for a digital camera that calculates the number of lenses necessary to photograph the sharpest image. Durand and Freeman previously worked with researchers to equip an ordinary camera lens with 12 miniature lenses that focus on different parts of an image. This eliminates the problem photographers face when focusing on one part of an image blurs the rest of it.
The new technique is particularly useful when photographers have little light or are far away from an object, Hasinoff says. The mathematical model uses factors such as the time limit, distance, and light to decide on the number of exposures necessary to create the clearest photograph. The algorithm also can combine the individual exposures into a coherent picture.
The fastest camera sensors today can create 60 images per second. This means that if the mathematical model needs to use more than six exposures per tenth of a second, it will be limited by the camera's slower speed. "But there's still a big gain to be had," Hasinoff says.
Stanford University professor Marc Levoy says the researchers have produced "a landmark paper." He says "there's been a lot of work on different ways of extending the depth of field, and what this paper did was, it tried to analyze all of them together."
From MIT News
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