The number of exposures necessary

Compressed sensing is an exciting new computational technique for extracting large amounts of information from a signal. In one high-profile demonstration, for instance, researchers at Rice University built a camera that could produce 2-D images using only a single light sensor rather than the millions of light sensors found in a commodity camera.

But using compressed sensing for image acquisition is inefficient: That “single-pixel camera” needed thousands of exposures to produce a reasonably clear image. Reporting their results in the journal IEEE Transactions on Computational Imaging, researchers from the MIT Media Lab now describe a new technique that makes image acquisition using compressed sensing 50 times as efficient. In the case of the single-pixel camera, it could get the number of exposures down from thousands to dozens.

One intriguing aspect of compressed-sensing imaging systems is that, unlike conventional cameras, they don’t require lenses. That could make them useful in harsh environments or in applications that use wavelengths of light outside the visible spectrum. Getting rid of the lens opens new prospects for the design of imaging systems.

“Formerly, imaging required a lens, and the lens would map pixels in space to sensors in an array, with everything precisely structured and engineered,” says Guy Satat, a graduate student at the Media Lab and first author on the new paper.  “With computational imaging, we began to ask: Is a lens necessary?  Does the sensor have to be a structured array? How many pixels should the sensor have? Is a single pixel sufficient? These questions essentially break down the fundamental idea of what a camera is.  The fact that only a single pixel is required and a lens is no longer necessary relaxes major design constraints, and enables the development of novel imaging systems. Using ultrafast sensing makes the measurement significantly more efficient.”

Recursive applications

One of Satat’s coauthors on the new paper is his thesis advisor, associate professor of media arts and sciences Ramesh Raskar. Like many projects from Raskar’s group, the new compressed-sensing technique depends on time-of-flight imaging, in which a short burst of light is projected into a scene, and ultrafast sensors measure how long the light takes to reflect back.

The technique uses time-of-flight imaging, but somewhat circularly, one of its potential applications is improving the performance of time-of-flight cameras. It could thus have implications for a number of other projects from Raskar’s group, such as a camera that can see around corners and visible-light imaging systems for medical diagnosis and vehicular navigation.

Many prototype systems from Raskar’s Camera Culture group at the Media Lab have used time-of-flight cameras called streak cameras, which are expensive and difficult to use: They capture only one row of image pixels at a time. But the past few years have seen the advent of commercial time-of-flight cameras called SPADs, for single-photon avalanche diodes.

Though not nearly as fast as streak cameras, SPADs are still fast enough for many time-of-flight applications, and they can capture a full 2-D image in a single exposure. Furthermore, their sensors are built using manufacturing techniques common in the computer chip industry, so they should be cost-effective to mass produce.