Researchers at UCLA (USA) have developed a new image projection system that integrates a convolutional neural network-based digital encoder with an all-optical passive diffractive decoder to deliver high-resolution image projections over an extended depth. A research team led by Professors Aydogan Ozcan and Mona Jarrahi, along with UCLA graduate student Hanlong Chen, designed a system that divides the 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.
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.
