the technology has now been introduced

Using a new algorithm, Stanford researchers reconstructed the movements of individual light particles to see through clouds, fog and other obstacles.

Researchers at Stanford University have developed a kind of X-ray vision, only without X-rays. With hardware similar to that that allows autonomous cars to “see” the world around them, researchers have improved their system with an extremely algorithm. efficient that can reconstruct hidden three-dimensional scenes, based on the movement of individual light particles or photons.

In tests conducted and published in Nature Communications, their system successfully reconstructed hidden shapes after a 1-inch layer of foam.

“A lot of imaging techniques make images look better, less ‘noisy,’ but this time, what we’ve developed is really a way to make the invisible visible,” said Gordon Wetzstein, assistant professor of electrical engineering at Stanford and lead author of the paper.

“This really pushes the boundaries of what could be possible with any kind of detection system. It’s like a superhuman vision. “

This technique complements other vision systems that can see through microscopic barriers – for medical applications – because it focuses more on large-scale situations, such as navigating cars with automatic driving in fog or heavy rains and satellite imaging of Earth’s surface and other planets through cloudy atmosphere.

Supercapacities of sight from scattered light

To see through environments that scatter light in each direction, the system combines a laser with a super-sensitive photon detector that records every bit of laser light that hits it. As the laser scans an obstruction like a foam wall, a photon will be able to pass through the foam, hit the hidden objects behind it, and go back through the foam to reach the detector.

The software supported by the algorithms then uses those few photons, and information about where and when they hit the detector, to reconstruct the hidden objects in 3D.

This is not the first system with the ability to reveal hidden objects through scattering media, but it circumvents the limitations associated with other techniques.

Some systems require information about how far the object of interest is or use only information from ballistic photons, which are photons that travel to and from the hidden object through the scattering field, but do not scatter along the way.

“We were interested in being able to take images through scattering media without these assumptions and collect all the photons that were scattered to reconstruct the image,” said David Lindell, a graduate student in electrical engineering and lead author of the paper.

“This makes our system particularly useful for large-scale applications, where there would be very few ballistic photons.”

To make their algorithm compatible with the complexity of the light scattering phenomenon, the researchers had to closely design their hardware and software, although the hardware components they used are only slightly more advanced than what is found. currently in autonomous cars.

Depending on the brightness of the hidden objects, the scan in their tests took from one minute to an hour, but the algorithm reconstructed the obscured scene in real time and could be run even on a laptop.

“You couldn’t see through the foam with your own eyes, and even if you look at the photon measurements at the detector, you really can’t see anything,” Lindell said. “But with just a handful of photons, the reconstruction algorithm can expose these objects – and you can see not only what they look like, but also where they are in 3D space.”

In the future, a descending version of this system could be sent into space to other planets to see through frozen clouds, deeper layers and surfaces. In the shorter term, researchers want to experiment with different scattering environments to simulate other circumstances in which this technology might be useful.

Share on facebook
Share on pinterest
Share on twitter
Share on linkedin
Share on email


Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.