More than 87 million Americans traveled internationally in 2017, a record number according to the U.S. National Travel and Tourism Office. If you were among them, perhaps you visited a destination such as Stonehenge, the Taj Mahal, Ha Long Bay, or the Great Wall of China.
And you might have used your phone to shoot a panorama, maybe even spinning yourself all the way around with your phone to shoot a super-wide, 360-degree view of the landscape.
If you were successful—meaning there were no misaligned sections, vignetting, or color shifts—then you experienced a simple yet effective example of computational photography. But in the past few years, computational photography has expanded beyond such narrow uses. It not only gives us a different perspective on photography but could also have an effect on how we view our world.
WHAT IS COMPUTATIONAL PHOTOGRAPHY?
Marc Levoy, professor of computer science (emeritus) at Stanford University, principal engineer at Google, and one of the pioneers in this emerging field, has defined computational photography as a variety of “computational imaging techniques that enhance or extend the capabilities of digital photography [in which the] output is an ordinary photograph, but one that could not have been taken by a traditional camera.”
According to Josh Haftel, principal product manager at Adobe, adding computational elements to traditional photography allows for new opportunities, particularly for imaging and software companies: “The way I see computational photography is that it gives us an opportunity to do two things. One of them is to try and shore up a lot of the physical limitations that exists within mobile cameras.”
Getting a smartphone to simulate shallow depth of field (DOF)—a hallmark of a professional-looking image, since it visually separates the subject from the background—is a good example. What prevents a camera on a very thin device, like a phone, from being able to capture an image with a shallow DOF? The laws of physics.
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