Micro-sized cameras have great potential to spot problems in the human body
and enable sensing for super-small robots, but past approaches captured
fuzzy, distorted images with limited fields of view.
Now, researchers at Princeton University and the University of Washington
have overcome these obstacles with an ultracompact camera the size of a
coarse grain of salt. The new system can produce crisp, full-color images on
par with a conventional compound camera lens 500,000 times larger in volume,
the researchers reported in a paper published Nov. 29 in Nature
Communications.
Enabled by a joint design of the camera's hardware and computational
processing, the system could enable minimally invasive endoscopy with
medical robots to diagnose and treat diseases, and improve imaging for other
robots with size and weight constraints. Arrays of thousands of such cameras
could be used for full-scene sensing, turning surfaces into cameras.
While a traditional camera uses a series of curved glass or plastic lenses
to bend light rays into focus, the new optical system relies on a technology
called a metasurface, which can be produced much like a computer chip. Just
half a millimeter wide, the metasurface is studded with 1.6 million
cylindrical posts, each roughly the size of the human immunodeficiency virus
(HIV).
Each post has a unique geometry, and functions like an optical antenna.
Varying the design of each post is necessary to correctly shape the entire
optical wavefront. With the help of machine learning-based algorithms, the
posts' interactions with light combine to produce the highest-quality images
and widest field of view for a full-color metasurface camera developed to
date.
A key innovation in the camera's creation was the integrated design of the
optical surface and the signal processing algorithms that produce the image.
This boosted the camera's performance in natural light conditions, in
contrast to previous metasurface cameras that required the pure laser light
of a laboratory or other ideal conditions to produce high-quality images,
said Felix Heide, the study's senior author and an assistant professor of
computer science at Princeton.
The researchers compared images produced with their system to the results of
previous metasurface cameras, as well as images captured by a conventional
compound optic that uses a series of six refractive lenses. Aside from a bit
of blurring at the edges of the frame, the nano-sized camera's images were
comparable to those of the traditional lens setup, which is more than
500,000 times larger in volume.
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Previous
micro-sized cameras (left) captured fuzzy, distorted images with
limited fields of view. A new system called neural nano-optics (right)
can produce crisp, full-color images on par with a conventional compound
camera lens. Credit: Princeton University. Other ultracompact metasurface lenses have suffered from major image
distortions, small fields of view, and limited ability to capture the full
spectrum of visible light—referred to as RGB imaging because it combines
red, green and blue to produce different hues.
"It's been a challenge to design and configure these little microstructures
to do what you want," said Ethan Tseng, a computer science Ph.D. student at
Princeton who co-led the study. "For this specific task of capturing large
field of view RGB images, it's challenging because there are millions of
these little microstructures, and it's not clear how to design them in an
optimal way. Co-lead author Shane Colburn tackled this challenge by creating a
computational simulator to automate testing of different nano-antenna
configurations. Because of the number of antennas and the complexity of
their interactions with light, this type of simulation can use "massive
amounts of memory and time," said Colburn. He developed a model to
efficiently approximate the metasurfaces' image production capabilities with
sufficient accuracy.
Colburn, who conducted the work as a Ph.D. student at the University of
Washington Department of Electrical & Computer Engineering (UW ECE),
where he is now an affiliate assistant professor. He also directs system
design at Tunoptix, a Seattle-based company that is commercializing
metasurface imaging technologies. Tunoptix was cofounded by Colburn's
graduate adviser Arka Majumdar, an associate professor at the University of
Washington in the ECE and physics departments and a coauthor of the study.
Coauthor James Whitehead, a Ph.D. student at UW ECE, fabricated the
metasurfaces, which are based on silicon nitride, a glass-like material that
is compatible with standard semiconductor manufacturing methods used for
computer chips—meaning that a given metasurface design could be easily
mass-produced at lower cost than the lenses in conventional cameras. "Although the approach to optical design is not new, this is the first
system that uses a surface optical technology in the front end and
neural-based processing in the back," said Joseph Mait, a consultant at
Mait-Optik and a former senior researcher and chief scientist at the U.S.
Army Research Laboratory.
"The significance of the published work is completing the Herculean task to
jointly design the size, shape and location of the metasurface's million
features and the parameters of the post-detection processing to achieve the
desired imaging performance," added Mait, who was not involved in the study.
Heide and his colleagues are now working to add more computational abilities
to the camera itself. Beyond optimizing image quality, they would like to
add capabilities for object detection and other sensing modalities relevant
for medicine and robotics.
Heide also envisions using ultracompact imagers to create "surfaces as
sensors." "We could turn individual surfaces into cameras that have
ultra-high resolution, so you wouldn't need three cameras on the back of
your phone anymore, but the whole back of your phone would become one giant
camera. We can think of completely different ways to build devices in the
future," he said. |
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