Super-resolution image display using diffractive decoders

• Published in: Science advances 2022-12, Vol.8 (48), p.eadd3433-eadd3433
• Main Authors: Işıl, Çağatay, Mengu, Deniz, Zhao, Yifan, Tabassum, Anika, Li, Jingxi, Luo, Yi, Jarrahi, Mona, Ozcan, Aydogan
• Format: Article
• Language: English
• Subjects: Applied Sciences and Engineering; ENGINEERING; MATERIALS SCIENCE; Physical and Materials Sciences; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.add3433

High-resolution image projection over a large field of view (FOV) is hindered by the restricted space-bandwidth product (SBP) of wavefront modulators. We report a deep learning-enabled diffractive display based on a jointly trained pair of an electronic encoder and a diffractive decoder to synthesize/project super-resolved images using low-resolution wavefront modulators. The digital encoder rapidly preprocesses the high-resolution images so that their spatial information is encoded into low-resolution patterns, projected via a low SBP wavefront modulator. The diffractive decoder processes these low-resolution patterns using transmissive layers structured using deep learning to all-optically synthesize/project super-resolved images at its output FOV. This diffractive image display can achieve a super-resolution factor of ~4, increasing the SBP by ~16-fold. We experimentally validate its success using 3D-printed diffractive decoders that operate at the terahertz spectrum. This diffractive image decoder can be scaled to operate at visible wavelengths and used to design large SBP displays that are compact, low power, and computationally efficient.