“It’s exciting to me because it changes the way we think,” says Bruce Flinchbaugh, manager of image and video processing at Texas Instruments. “It’s not very often in a field like imaging that somebody comes along and does something so different to solve a problem.”
The researcher’s camera has a long way to go before it’s in a commercialized form, though, notes Baraniuk. Right now, the setup spans an optical table in a lab, and the researchers’ algorithms are slow compared with the compression in commercial cameras. The group is working to make its algorithms faster, and, Baraniuk adds, the hardware continues to improve as more micromirrors are being added to smaller arrays, and their flipping speed increases.
Baraniuk expects that the first application for the new camera could be in terahertz imaging systems–systems that use terahertz-frequency radiation to see through objects and detect small amounts of chemicals. Currently, it’s expensive to build the large sensors needed for these systems, he says, so a single-sensor camera like the one the group developed would be ideal.
Eventually, Rice’s Kelly envisions a version of the group’s algorithm being used in commercial cameras. This could reduce the number of sensors in such a product–decreasing its size and cost–while increasing the overall resolution of pictures. “You might buy a camera with a 2-megapixel sensor, but [the software] might give you a 20- or 30-megapixel image,” he says. “You could exploit the math in a way to allow your pocket camera to give you a much nicer picture.”