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LOS ANGELES - Californer -- Researchers from the University of California, Los Angeles (UCLA) has unveiled a new optical imaging technology that could significantly enhance visual information processing and communication systems. The new system, based on partially coherent unidirectional imaging, offers a compact, efficient solution for transmitting visual data in one direction while blocking transmission in the opposite direction.
This innovative technology, led by Professor Aydogan Ozcan, is designed to selectively transmit high-quality images in one direction, from field-of-view A to field-of-view B, while deliberately distorting images when viewed from the reverse direction, B to A. This asymmetric image transmission could have broad implications for fields like privacy protection, augmented reality, and optical communications, offering new capabilities for managing how visual optical information is processed and transmitted.
The new system addresses a challenge in optical engineering: how to control light transmission to enable clear imaging in one direction while blocking it in the reverse. Previous solutions for unidirectional wave transmission have often relied on complex methods such as temporal modulation, nonlinear materials, or high-power beams under fully coherent illumination, which limit their practical applications. In contrast, this UCLA innovation leverages partially coherent light to achieve high image quality and power efficiency in the forward direction, while intentionally introducing distortion and reduced power efficiency in the reverse direction.
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A key aspect of this development is the use of deep learning to physically design the diffractive layers that make up the unidirectional imaging system. These layers are optimized for partially coherent light with a phase correlation length greater than 1.5 times the wavelength of the light. This careful optimization ensures that the system provides reliable unidirectional image transmission, even when the light source has varying coherence properties. Each imager is compact, axially spanning less than 75 wavelengths, and features a polarization-independent design. The deep learning algorithms used in the design process help ensure that the system maintains high diffraction efficiency in the forward direction while suppressing image formation in the reverse.
The researchers demonstrated that their system performs consistently across different image datasets and illumination conditions, showing resilience to changes in the light's coherence properties. Looking ahead, the researchers plan to extend the unidirectional imager to different parts of the spectrum, including infrared and visible ranges, and to explore various kinds of illumination sources. These advancements could push the boundaries of imaging and sensing, unlocking new applications and innovations. In privacy protection, for example, the technology could be used to prevent sensitive information from being visible from unintended perspectives. Similarly, augmented and virtual reality systems could use this technology to control how information is displayed to different viewers.
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This research was conducted by an interdisciplinary team from UCLA's Department of Electrical and Computer Engineering and California NanoSystems Institute (CNSI).
Publication: https://doi.org/10.1117/1.APN.3.6.066008
This innovative technology, led by Professor Aydogan Ozcan, is designed to selectively transmit high-quality images in one direction, from field-of-view A to field-of-view B, while deliberately distorting images when viewed from the reverse direction, B to A. This asymmetric image transmission could have broad implications for fields like privacy protection, augmented reality, and optical communications, offering new capabilities for managing how visual optical information is processed and transmitted.
The new system addresses a challenge in optical engineering: how to control light transmission to enable clear imaging in one direction while blocking it in the reverse. Previous solutions for unidirectional wave transmission have often relied on complex methods such as temporal modulation, nonlinear materials, or high-power beams under fully coherent illumination, which limit their practical applications. In contrast, this UCLA innovation leverages partially coherent light to achieve high image quality and power efficiency in the forward direction, while intentionally introducing distortion and reduced power efficiency in the reverse direction.
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A key aspect of this development is the use of deep learning to physically design the diffractive layers that make up the unidirectional imaging system. These layers are optimized for partially coherent light with a phase correlation length greater than 1.5 times the wavelength of the light. This careful optimization ensures that the system provides reliable unidirectional image transmission, even when the light source has varying coherence properties. Each imager is compact, axially spanning less than 75 wavelengths, and features a polarization-independent design. The deep learning algorithms used in the design process help ensure that the system maintains high diffraction efficiency in the forward direction while suppressing image formation in the reverse.
The researchers demonstrated that their system performs consistently across different image datasets and illumination conditions, showing resilience to changes in the light's coherence properties. Looking ahead, the researchers plan to extend the unidirectional imager to different parts of the spectrum, including infrared and visible ranges, and to explore various kinds of illumination sources. These advancements could push the boundaries of imaging and sensing, unlocking new applications and innovations. In privacy protection, for example, the technology could be used to prevent sensitive information from being visible from unintended perspectives. Similarly, augmented and virtual reality systems could use this technology to control how information is displayed to different viewers.
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This research was conducted by an interdisciplinary team from UCLA's Department of Electrical and Computer Engineering and California NanoSystems Institute (CNSI).
Publication: https://doi.org/10.1117/1.APN.3.6.066008
Source: ucla ita
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