New generation of superlattice cameras add more 'color' to night vision

ScienceDaily (Oct. 20, 2011) — Recent breakthroughs have enabled scientists from the Northwestern University's Center for Quantum Devices to build cameras that can see more than one optical waveband or "color" in the dark. The semiconducting material used in the cameras -- called type-II superlattices -- can be tuned to absorb a wide range of infrared wavelengths, and now, a number of distinct infrared bands at the same time.

The idea of capturing light simultaneously at different wavelengths isn't new. Digital cameras in the visible spectrum are commonly equipped with detectors that sense red, green, and blue light to replicate a vast majority of colors perceived by the human eye. Multi-color detection in the infrared spectrum, however, offers unique functionalities beyond color representation. The resonant frequencies of compounds can often be found in this spectral range, which means that chemical spectroscopy can be relayed in images real-time.

"When coupled with image-processing algorithms performed on multiple wavebands, the amount of information rendered in a particular scene is tremendous," said Manijeh Razeghi, Walter P. Murphy Professor in Electrical Engineering and Computer Science at the McCormick School of Engineering and director of the Center for Quantum Devices.

Razeghi's group engineered the detection energies on the cameras to be extremely narrow, close to one-tenth of an electron volt, in what is known as the long-wave infrared window. Creating the cameras was difficult, however, because the light-absorbing layers are prone to parasitic effects. Furthermore, the detectors were designed to be stacked one on top of another, which provided spatially coincident pixel registration but added significantly to the growth and fabrication challenges. Nevertheless, a dual-band long-wave infrared 320-by-256 sized type-II superlattice camera was demonstrated for the first time in the world, the results of which were published in the July 2011 issue of Optics Letters.

Such infrared photon cameras based on another material called HgCdTe were used in disaster relief in March 2011 when a catastrophic tsunami damaged Japans' nuclear reactors. These cameras provided accurate temperature information about the reactors from unmanned aerial vehicles, providing officials the information they needed to orchestrate cooling efforts and prevent nuclear meltdown.

HgCdTe, however, is considered to be an expensive technology in the long-wave infrared due to its poor spectral uniformity and therefore yield -- areas in which type-II superlattices may prove more efficient.

"Type-II superlattices can be grown uniformly even at very long-wavelengths because its energy gap is determined by the alternating InAs and GaSb quantum well thicknesses, rather than its composition as is the case with HgCdTe," Razeghi said. The high-resolution multi-band type-II superlattice camera also offered very impressive performances, requiring only 0.5 milliseconds to capture a frame with temperature sensitivities as good as 0.015°C. "The high-performance, multi-functionality, and low cost offered by type-II superlattices truly make it an attractive infrared technology," she added.

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Journal Reference:

Edward Kwei-wei Huang, Abbas Haddadi, Guanxi Chen, Binh-Minh Nguyen, Minh-Anh Hoang, Ryan McClintock, Mark Stegall, Manijeh Razeghi. Type-II superlattice dual-band LWIR imager with M-barrier and Fabry–Perot resonance. Optics Letters, 2011; 36 (13): 2560 DOI: 10.1364/OL.36.002560

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Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

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New generation of superlattice cameras add more 'color' to night vision

ScienceDaily (Oct. 20, 2011) — Recent breakthroughs have enabled scientists from the Northwestern University's Center for Quantum Devices to build cameras that can see more than one optical waveband or "color" in the dark. The semiconducting material used in the cameras -- called type-II superlattices -- can be tuned to absorb a wide range of infrared wavelengths, and now, a number of distinct infrared bands at the same time.

The idea of capturing light simultaneously at different wavelengths isn't new. Digital cameras in the visible spectrum are commonly equipped with detectors that sense red, green, and blue light to replicate a vast majority of colors perceived by the human eye. Multi-color detection in the infrared spectrum, however, offers unique functionalities beyond color representation. The resonant frequencies of compounds can often be found in this spectral range, which means that chemical spectroscopy can be relayed in images real-time.

"When coupled with image-processing algorithms performed on multiple wavebands, the amount of information rendered in a particular scene is tremendous," said Manijeh Razeghi, Walter P. Murphy Professor in Electrical Engineering and Computer Science at the McCormick School of Engineering and director of the Center for Quantum Devices.

Razeghi's group engineered the detection energies on the cameras to be extremely narrow, close to one-tenth of an electron volt, in what is known as the long-wave infrared window. Creating the cameras was difficult, however, because the light-absorbing layers are prone to parasitic effects. Furthermore, the detectors were designed to be stacked one on top of another, which provided spatially coincident pixel registration but added significantly to the growth and fabrication challenges. Nevertheless, a dual-band long-wave infrared 320-by-256 sized type-II superlattice camera was demonstrated for the first time in the world, the results of which were published in the July 2011 issue of Optics Letters.

Such infrared photon cameras based on another material called HgCdTe were used in disaster relief in March 2011 when a catastrophic tsunami damaged Japans' nuclear reactors. These cameras provided accurate temperature information about the reactors from unmanned aerial vehicles, providing officials the information they needed to orchestrate cooling efforts and prevent nuclear meltdown.

HgCdTe, however, is considered to be an expensive technology in the long-wave infrared due to its poor spectral uniformity and therefore yield -- areas in which type-II superlattices may prove more efficient.

"Type-II superlattices can be grown uniformly even at very long-wavelengths because its energy gap is determined by the alternating InAs and GaSb quantum well thicknesses, rather than its composition as is the case with HgCdTe," Razeghi said. The high-resolution multi-band type-II superlattice camera also offered very impressive performances, requiring only 0.5 milliseconds to capture a frame with temperature sensitivities as good as 0.015°C. "The high-performance, multi-functionality, and low cost offered by type-II superlattices truly make it an attractive infrared technology," she added.

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The above story is reprinted from materials provided by Northwestern University.

Note: ScienceDaily reserves the right to edit materials for content and length. For further information, please contact the source cited above.

Journal Reference:

Edward Kwei-wei Huang, Abbas Haddadi, Guanxi Chen, Binh-Minh Nguyen, Minh-Anh Hoang, Ryan McClintock, Mark Stegall, Manijeh Razeghi. Type-II superlattice dual-band LWIR imager with M-barrier and Fabry–Perot resonance. Optics Letters, 2011; 36 (13): 2560 DOI: 10.1364/OL.36.002560

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Researchers develop optimal algorithm for determining focus error in eyes and cameras

ScienceDaily (Sep. 26, 2011) — University of Texas at Austin researchers have discovered how to extract and use information in an individual image to determine how far objects are from the focus distance, a feat only accomplished by human and animal visual systems until now.

Like a camera, the human eye has an auto-focusing system, but human auto-focusing rarely makes mistakes. And unlike a camera, humans do not require trial and error to focus an object.

Johannes Burge, a postdoctoral fellow in the College of Liberal Arts' Center for Perceptual Systems and co-author of the study, says it is significant that a statistical algorithm can now determine focus error, which indicates how much a lens needs to be refocused to make the image sharp, from a single image without trial and error.

"Our research on defocus estimation could deepen our understanding of human depth perception," Burge says. "Our results could also improve auto-focusing in digital cameras. We used basic optical modeling and well-understood statistics to show that there is information lurking in images that cameras have yet to tap."

The researchers' algorithm can be applied to any blurry image to determine focus error. An estimate of focus error also makes it possible to determine how far objects are from the focus distance.

In the human eye, inevitable defects in the lens, such as astigmatism, can help the visual system (via the retina and brain) compute focus error; the defects enrich the pattern of "defocus blur," the blur that is caused when a lens is focused at the wrong distance. Humans use defocus blur to both estimate depth and refocus their eyes. Many small animals use defocus as their primary depth cue.

"We are now one step closer to understanding how these feats are accomplished," says Wilson Geisler, director of the Center for Perceptual Systems and coauthor of the study. "The pattern of blur introduced by focus errors, along with the statistical regularities of natural images, makes this possible."

Burge and Geisler considered what happens to images as focus error increases: an increasing amount of detail is lost with larger errors. Then, they noted that even though the content of images varies considerably (e.g. faces, mountains, flowers), the pattern and amount of detail in images is remarkably constant. This constancy makes it possible to determine the amount of defocus and, in turn, to re-focus appropriately.

Their article was recently published in the Proceedings of the National Academy of Sciences. The research was supported by a grant from the National Institutes of Health.

The Center for Perceptual Systems is an integrated program that overlaps several separate departments: Neuroscience, Psychology, Electrical and Computer Engineering, Neurobiology, Computer Science, and Speech and Communication.

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The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

J. Burge, W. S. Geisler. Optimal defocus estimation in individual natural images. Proceedings of the National Academy of Sciences, 2011; DOI: 10.1073/pnas.1108491108

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Researchers develop optimal algorithm for determining focus error in eyes and cameras

ScienceDaily (Sep. 26, 2011) — University of Texas at Austin researchers have discovered how to extract and use information in an individual image to determine how far objects are from the focus distance, a feat only accomplished by human and animal visual systems until now.

Like a camera, the human eye has an auto-focusing system, but human auto-focusing rarely makes mistakes. And unlike a camera, humans do not require trial and error to focus an object.

Johannes Burge, a postdoctoral fellow in the College of Liberal Arts' Center for Perceptual Systems and co-author of the study, says it is significant that a statistical algorithm can now determine focus error, which indicates how much a lens needs to be refocused to make the image sharp, from a single image without trial and error.

"Our research on defocus estimation could deepen our understanding of human depth perception," Burge says. "Our results could also improve auto-focusing in digital cameras. We used basic optical modeling and well-understood statistics to show that there is information lurking in images that cameras have yet to tap."

The researchers' algorithm can be applied to any blurry image to determine focus error. An estimate of focus error also makes it possible to determine how far objects are from the focus distance.

In the human eye, inevitable defects in the lens, such as astigmatism, can help the visual system (via the retina and brain) compute focus error; the defects enrich the pattern of "defocus blur," the blur that is caused when a lens is focused at the wrong distance. Humans use defocus blur to both estimate depth and refocus their eyes. Many small animals use defocus as their primary depth cue.

"We are now one step closer to understanding how these feats are accomplished," says Wilson Geisler, director of the Center for Perceptual Systems and coauthor of the study. "The pattern of blur introduced by focus errors, along with the statistical regularities of natural images, makes this possible."

Burge and Geisler considered what happens to images as focus error increases: an increasing amount of detail is lost with larger errors. Then, they noted that even though the content of images varies considerably (e.g. faces, mountains, flowers), the pattern and amount of detail in images is remarkably constant. This constancy makes it possible to determine the amount of defocus and, in turn, to re-focus appropriately.

Their article was recently published in the Proceedings of the National Academy of Sciences. The research was supported by a grant from the National Institutes of Health.

The Center for Perceptual Systems is an integrated program that overlaps several separate departments: Neuroscience, Psychology, Electrical and Computer Engineering, Neurobiology, Computer Science, and Speech and Communication.

Recommend this story on Facebook, Twitter,
and Google +1:

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Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

J. Burge, W. S. Geisler. Optimal defocus estimation in individual natural images. Proceedings of the National Academy of Sciences, 2011; DOI: 10.1073/pnas.1108491108

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here