From picture to 3D hologram: end-to-end learning of real-time 3D photorealistic hologram generation from 2D image input.

In this Letter, we demonstrate a deep-learning-based method capable of synthesizing a photorealistic 3D hologram in real-time directly from the input of a single 2D image. We design a fully automatic pipeline to create large-scale datasets by converting any collection of real-life images into pairs of 2D images and corresponding 3D holograms and train our convolutional neural network (CNN) end-to-end in a supervised way. Our method is extremely computation-efficient and memory-efficient for 3D hologram generation merely from the knowledge of on-hand 2D image content. We experimentally demonstrate speckle-free and photorealistic holographic 3D displays from a variety of scene images, opening up a way of creating real-time 3D holography from everyday pictures.

Three-dimensional computer holography enabled from a single 2D image.

To compute a high-quality computer-generated hologram (CGH) for true 3D real scenes, a huge amount of 3D data must be physically acquired and provided depending on specific devices or 3D rendering techniques. Here, we propose a computational framework for generating a CGH from a single image based on the idea of 2D-to-3D wavefront conversion. We devise a deep view synthesis neural network to synthesize light-field contents from a single image and convert the light-field data to the diffractive wavefront of the hologram using a ray-wave algorithm. The method is able to achieve extremely straightforward 3D CGH generation from hand-accessible 2D image content and outperforms existing real-world-based CGH computation, which inevitably relies on a high-cost depth camera and cumbersome 3D data rendering. We experimentally demonstrate 3D reconstructions of indoor and outdoor scenes from a single image enabled phase-only CGH.

Deep-learning-based computer-generated hologram from a stereo image pair.

We propose a deep-learning-based approach to producing computer-generated holograms (CGHs) of real-world scenes. We design an end-to-end convolutional neural network (the Stereo-to-Hologram Network, SHNet) framework that takes a stereo image pair as input and efficiently synthesizes a monochromatic 3D complex hologram as output. The network is able to rapidly and straightforwardly calculate CGHs from the directly recorded images of real-world scenes, eliminating the need for time-consuming intermediate depth recovery and diffraction-based computations. We demonstrate the 3D reconstructions with clear depth cues obtained from the SHNet-based CGHs by both numerical simulations and optical holographic virtual reality display experiments.

In vitro enzymatic electrochemical monitoring of glucose metabolism and production in rat primary hepatocytes on highly O2 permeable plates.

In situ continuous glucose monitoring under physiological culture conditions is imperative in understanding the dynamics of cell and tissue behaviors and their physiological responses since glucose plays an important role in principal source of biological energy. We therefore examined physiologically relevant dynamic changes in glucose levels based on glucose metabolism and production during aerobic culture (10% O2) of rat primary hepatocytes stimulated with insulin or glucagon on a highly O2 permeable plate, which can maintain the oxygen concentration close to the periportal zone of the liver. As glucose monitoring devices, we used oxygen-independent glucose dehydrogenase-modified single-walled carbon nanotube electrodes placed close to the surface of the hepatocytes. The current response of glucose oxidation slightly decreased after the addition of insulin in the presence of glucose due to the acceleration of glucose uptake by the hepatocytes, whereas that significantly increased after the addition of glucagon and fructose even in the absence of glucose due to the conversion of fructose to glucose based on gluconeogenesis. These phenomena might be consistent relatively with the physiological behaviors of hepatocytes in the periportal region. The present monitoring system would be useful for the studies of glucose homeostasis and diabetes in vitro.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

A mitochondrial revelation of early human migrations to the Tibetan Plateau before and after the last glacial maximum.

As the highest plateau surrounded by towering mountain ranges, the Tibetan Plateau was once considered to be one of the last populated areas of modern humans. However, this view has been tremendously changed by archeological, linguistic, and genetic findings in the past 60 years. Nevertheless, the timing and routes of entry of modern humans into the Tibetan Plateau is still unclear. To make these problems clear, we carried out high-resolution mitochondrial-DNA (mtDNA) analyses on 562 Tibeto-Burman inhabitants from nine different regions across the plateau. By examining the mtDNA haplogroup distributions and their principal components, we demonstrated that maternal diversity on the plateau reflects mostly a northern East Asian ancestry. Furthermore, phylogeographic analysis of plateau-specific sublineages based on 31 complete mtDNA sequences revealed two primary components: pre-last glacial maximum (LGM) inhabitants and post-LGM immigrants. Also, the analysis of one major pre-LGM sublineage A10 showed a strong signal of post-LGM population expansion (about 15,000 years ago) and greater diversity in the southern part of the Tibetan Plateau, indicating the southern plateau as a refuge place when climate dramatically changed during LGM.