Işıl Çağatay, Mengu Deniz, Zhao Yifan, Tabassum Anika, Li Jingxi, Luo Yi, Jarrahi Mona, Ozcan Aydogan
Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
Bioengineering Department, University of California, Los Angeles, CA 90095, USA.
Sci Adv. 2022 Dec 2;8(48):eadd3433. 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.
波前调制器有限的空间带宽积（SBP）阻碍了在大视场（FOV）上进行高分辨率图像投影。我们报告了一种基于深度学习的衍射显示器，它由一对联合训练的电子编码器和衍射解码器组成，用于使用低分辨率波前调制器合成/投影超分辨率图像。数字编码器对高分辨率图像进行快速预处理，以便将其空间信息编码为低分辨率图案，通过低SBP波前调制器进行投影。衍射解码器使用通过深度学习构建的透射层来处理这些低分辨率图案，以便在其输出视场中全光合成/投影超分辨率图像。这种衍射图像显示器可以实现约4倍的超分辨率因子，将SBP提高约16倍。我们使用在太赫兹光谱下工作的3D打印衍射解码器对其成功进行了实验验证。这种衍射图像解码器可以扩展到在可见光波长下工作，并用于设计紧凑、低功耗且计算效率高的大SBP显示器。
