Other researchers are headed toward similar depth-map goals from different approaches. Some use intelligent software to inspect ordinary 2-D photos for the edges, shadows or focus differences that might infer the distances of objects. Others have tried cameras with multiple lenses, or prisms mounted in front of a single camera lens. One approach employs lasers; another attempts to stitch together photos taken from different angles, while yet another involves video shot from a moving camera.
But El Gamal, Fife and Wong believe their multi-aperture sensor has some key advantages. Its small and doesnt require lasers, bulky camera gear, multiple photos or complex calibration. And it has excellent color quality. Each of the 256 pixels in a specific array detects the same color. In an ordinary digital camera, red pixels may be arranged next to green pixels, leading to undesirable crosstalk between the pixels that degrade color.
The sensor also can take advantage of smaller pixels in a way that an ordinary digital camera cannot, El Gamal said, because camera lenses are nearing the optical limit of the smallest spot they can resolve. Using a pixel smaller than that spot will not produce a better photo. But with the multi-aperture sensor, smaller pixels produce even more depth information, he said.
The technology also may aid the quest for the huge photos possible with a gigapixel camerathats 140 times as many pixels as todays typical 7-megapixel cameras. The first benefit of the Stanford technology is straightforward: Smaller pixels mean more pixels can be crowded onto the chip.
The second benefit involves chip architecture. With a billion pixels on one chip, some of them are sure to go bad, leaving dead spots, El Gamal said. But the overlapping views provided by the multi-aperture sensor provide backups when pixels fail.
The researchers are now working out the manufacturing d
|Contact: Abbas El Gamal|