Taking aim at far-from-perfect photos

Bryn Nelson Columnist

One software program can merge multiple camera shots to eliminate that half-dazed look that always seems to afflict one person in every group photo. Another can combine a picture taken with and without a flash to pair your smiling face with a properly illuminated nighttime scene. Yet another program can pull out crystal-clear details by shifting the focus after you've snapped a picture. And who wouldn’t want a camera that can clarify an otherwise blurry image like a speeding bicyclist?

With a few exceptions, none of these options have yet made their way into widely available cameras. But the fast-moving field of computational photography and an open-source “Frankencamera” built by Marc Levoy and his graduate students at Stanford University are suggesting how shutterbugs in the near future may be able to swap in and out the features they want with little more trouble than snapping together LEGO parts.

“You could think of them as LEGO Mindstorms cameras,” said Levoy, referring to LEGO’s advanced building sets that have become a favorite tool for engineers and robotics experts. “With hardware and software and algorithms, there’s just no end to what we can do with cameras and computational photography. I think it’s going to be very exciting.”

The annual SIGGRAPH conferences (short for Special Interest Group on GRAPHics and Interactive Techniques) convened by the Association for Computing Machinery have been particularly fertile ground for the field. Among the innovations debuting at this year’s conference, Bill Freeman and colleagues at MIT’s Computer Science and Artificial Intelligence Lab introduced a technique called motion-invariant photography.

“Counterintuitively but elegantly, if you move the camera during the exposure in a particular way, you can make an image that is all blurred, but in a way that’s easy to de-blur,” Freeman said.

The method works well for clarifying objects that are moving horizontally (think racing bicyclists), and Freeman is working on extending the range to two dimensions.

The technique, he said, could be used in a software program like Adobe Photoshop or added to a camera as a peripheral.

“The beauty of it is that it’s a simple de-blur, so you could do it in the camera,” he said. And by doing something new with both the camera and the computation, “that opens up a whole new design space.”

Finding a sharper image
Another emerging feature, known as digital refocusing, allows a camera to snap a group shot of well-wishers lined up along a hallway, successively capturing each face down the line with its shifting focus.

Similarly, a refocused portrait of someone in front of a window could be fixed if, say, you captured the Venetian blinds in exquisite detail while your girlfriend’s face is a fuzzy blob.

With digital refocusing, developed by Levoy in collaboration with fellow Stanford computer scientist Pat Hanrahan and former graduate student Ren Ng, photographers first capture the necessary data with a micro-lens array built into the camera between the photo sensor and the main lens. Then after the fact, they can refocus the shot by changing which pixels are added together.

“Or you could imagine doing different computations that would put everything into focus,” Levoy said. “You can get a nice artistic effect, but sometimes you just want everyone sharp.” (Ng’s Refocus Imaging, Inc. has some sample photos.)

Another common theme in computational photography has been to combine the benefits of flash and no-flash photography to create a composite image in which both the foreground and background are properly lit.

A no-flash photograph may capture a night scene well, while hiding your foreground subject in shadows. A flash version lights up his face — and his reflection in the window, obscuring the interesting street scene behind him. But an intelligent combination of the two? Priceless.

“If you think about traditional photography, it’s taking the light in a scene and sampling it and converting it into an image that hopefully looks like what you saw when you were standing there,” said Aseem Agarwala, a senior research scientist at San Jose, Calif.-based Adobe Systems.

But what if the light was too low, or you moved the camera during the shot, or your subject had her eyes closed during the one-sixtieth of a second that your camera actually sampled the scene?

“In computational photography,” he said, “we’ve put a computer between the sampling process and the final output.” The result is an ever-growing range of features that can sample far more information and yield impressive images under challenging conditions.

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