Deep learning-based computer-generated holography (CGH) has recently demonstrated tremendous potential in three-dimensional (3D) displays and yielded impressive display quality. However, current CGH techniques are mostly limited on generating and transmitting holograms with a resolution of 1080p, which is far from the ultra-high resolution (16K+) required for practical virtual reality (VR) and augmented reality (AR) applications to support a wide field of view and large eye box. One of the major obstacles in current CGH frameworks lies in the limited memory available on consumer-grade GPUs which could not facilitate the generation of highdefinition holograms. Moreover, the existing hologram compression rate can hardly permit the transmission of high-resolution holograms over a 5G communication network, which is crucial for mobile application. To overcome the aforementioned challenges, we proposed an efficient joint framework for hologram generation and transmission to drive the development of consumer-grade high-definition holographic displays. Specifically, for hologram generation, we proposed a plug-and-play module that includes a pixel shuffle layer and a lightweight holographic super-resolution network, enabling the current CGH networks to generate high-definition holograms. For hologram transmission, we presented an efficient holographic transmission framework based on foveated rendering. In simulations, we have successfully achieved the generation and transmission of holograms with a 4K resolution for the first time on an NVIDIA GeForce RTX 3090 GPU. We believe the proposed framework could be a viable approach for the evergrowing data issue in holographic displays.
Current learning-based Computer-Generated Holography (CGH) algorithms often utilize Convolutional Neural Networks (CNN)-based architectures. However, the CNN-based non-iterative methods mostly underperform the State-Of-The-Art (SOTA) iterative algorithms such as Stochastic Gradient Descent (SGD) in terms of display quality. Inspired by the global attention mechanism of Vision Transformer (ViT), we propose a novel unsupervised autoencoder-based ViT for generating phase-only holograms. Specifically, for the encoding part, we use Uformer to generate the holograms. For the decoding part, we use the Angular Spectrum Method (ASM) instead of a learnable network to reconstruct the target images. To validate the effectiveness of the proposed method, numerical simulations and optical reconstructions are performed to compare our proposal against both iterative algorithms and CNN-based techniques. In the numerical simulations, the PSNR and SSIM of the proposed method are 26.78 dB and 0.832, which are 4.02 dB and 0.09 higher than that of the CNN-based method, respectively. Moreover, the proposed method contains less speckles and features a higher display quality than other CGH methods in experiments. We suggest the improvement might be ascribed to the ViT’s global attention mechanism, which is more suitable for learning the cross-domain mapping from image (spatial) domain to hologram (Fourier) domain. We believe the proposed ViT-based CGH algorithm could be a promising candidate for future real-time high-fidelity holographic displays.
Iterative methods could provide high-quality image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) by solving an inverse problem. Compared with the regular IFFT-based reconstruction, a more accurate estimation could be iteratively solved by integrating prior knowledge, however, it is often more time-consuming. To deal with the time problem, we proposed a fast iterative method for FD-OCT image reconstruction empowered by GPU acceleration. An iterative scheme is adopted, including a forward model and an inverse solver. Large-scale parallelism of OCT image reconstruction is performed on B-scans. We deployed the framework on Nvidia GeForce RTX 3090 graphic card that enables parallel processing. With the widely used toolkit Pytorch, the inverse problem of OCT image reconstruction is solved by the stochastic gradient descent (SGD) algorithm. To validate the effectiveness of the proposed method, we compare the computational time and image quality with other iterative approaches including ADMM, AR, and RFIAA method. The proposed method could provide a significant speed enhancement of 1,500 times with comparable image quality to that of ADMM reconstruction. The result indicates a potential for high-quality real-time volumetric OCT image reconstruction via iterative algorithms.
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