Optimization of DMD-based independent amplitude and phase modulation by analysis of target complex wavefront

Physical twinning for joint encoding-decoding optimization in computational optics: a review

Computational optics introduces computation into optics and consequently helps overcome traditional optical limitations such as low sensing dimension, low light throughput, low resolution, and so on. The combination of optical encoding and computational decoding offers enhanced imaging and sensing capabilities with diverse applications in biomedicine, astronomy, agriculture, etc. With the great advance of artificial intelligence in the last decade, deep learning has further boosted computational optics with higher precision and efficiency. Recently, there developed an end-to-end joint optimization technique that digitally twins optical encoding to neural network layers, and then facilitates simultaneous optimization with the decoding process. This framework offers effective performance enhancement over conventional techniques. However, the reverse physical twinning from optimized encoding parameters to practical modulation elements faces a serious challenge, due to the discrepant gap in such as bit depth, numerical range, and stability. In this regard, this review explores various optical modulation elements across spatial, phase, and spectral dimensions in the digital twin model for joint encoding-decoding optimization. Our analysis offers constructive guidance for finding the most appropriate modulation element in diverse imaging and sensing tasks concerning various requirements of precision, speed, and robustness. The review may help tackle the above twinning challenge and pave the way for next-generation computational optics.

High-fidelity and high-speed wavefront shaping by leveraging complex media

High-precision light manipulation is crucial for delivering information through complex media. However, existing spatial light modulation devices face a fundamental speed-fidelity tradeoff. Digital micromirror devices have emerged as a promising candidate for high-speed wavefront shaping but at the cost of compromised fidelity due to the limited control degrees of freedom. Here, we leverage the sparse-to-random transformation through complex media to overcome the dimensionality limitation of spatial light modulation devices. We demonstrate that pattern compression by sparsity-constrained wavefront optimization allows sparse and robust wavefront representations in complex media, improving the projection fidelity without sacrificing frame rate, hardware complexity, or optimization time. Our method is generalizable to different pattern types and complex media, supporting consistent performance with up to 89% and 126% improvements in projection accuracy and speckle suppression, respectively. The proposed optimization framework could enable high-fidelity high-speed wavefront shaping through different scattering media and platforms without changes to the existing holographic setups, facilitating a wide range of physics and real-world applications.

Real-time 4K computer-generated hologram based on encoding conventional neural network with learned layered phase

Learning-based computer-generated hologram (CGH) demonstrates great potential for real-time high-quality holographic displays. However, real-time 4K CGH generation for 3D scenes remains a challenge due to the computational burden. Here, a variant conventional neural network (CNN) is presented for CGH encoding with learned layered initial phases for layered CGH generation. Specifically, the CNN predicts the CGH based on the input complex amplitude on the CGH plane, and the learned initial phases act as a universal phase for any target images at the target depth layer. These phases are generated during the training process of the coding CNN to further optimize the quality. The CNN is trained to learn encoding 3D CGH by randomly selecting the depth layer in the training process, and contains only 938 parameters. The generation time for a 2D 4K CGH is 18 ms, and is increased by 12 ms for each layer in a layered 3D scene. The average Peak Signal to Noise Ratio (PSNR) of each layer is above 30dB in the depth range from 160 to 210 mm. Experiments verify that our method can achieve real-time layered 4K CGH generation.