LOS ANGELES - eTradeWire -- Researchers at the University of California, Los Angeles (UCLA) have developed a novel image projection system that delivers super-resolution images over an extended depth of field. By combining a neural network-based digital encoder with a passive all-optical diffractive decoder, the system drastically compresses image data for efficient transmission of image information. This platform operates without extra power at the decoding stage, promising advancements for next-generation virtual and augmented reality displays.

A research team led by Professors Aydogan Ozcan and Mona Jarrahi, along with UCLA graduate student Hanlong Chen, designed a system that divides the image projection workload into two parts. First, a digital encoder compresses input images into highly compact phase representations, significantly reducing the required data footprint; after this encoding, the resulting compressed patterns are displayed by a low-resolution phase projector. Second, an analog diffractive decoder processes these phase patterns using passive, static optical layers to reconstruct high-resolution output images. Because the optical decoder is entirely passive, it synthesizes super-resolved images without requiring additional power consumption.

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The team successfully validated their approach through proof-of-concept experiments in both the terahertz and visible parts of the spectrum. The hybrid platform demonstrated high-fidelity image synthesis over an extended depth. Furthermore, the system achieved up to a 16-fold improvement in the space-bandwidth product at each lateral plane, bypassing the constraints of the input display. Tests also proved the system's robust external generalization, as it successfully projected unseen object/data classes and maintained image projection quality despite structural misalignments, experimental imperfections or phase quantization constraints.

By significantly reducing data storage and image transmission requirements without imposing additional power constraints through the passive optical decoder, this diffractive architecture provides a powerful pathway for next-generation image display systems.

Authors of this work, published in Light: Science & Applications, include Hanlong Chen, Cagatay Isıl, Che-Yung Shen, Shiqi Chen, Tianyi Gan, Mona Jarrahi and Aydogan Ozcan – all from UCLA, USA.

Reference: https://doi.org/10.1038/s41377-026-02320-7