An augmented reality (AR) near-eye display using Pancharatnam-Berry (PB) phase lenses is proposed. PB phase lenses provide different optical effects depending on the polarization state of the ...incident light. By exploiting this characteristic, it is possible to manufacture an AR combiner with a small form factor and a large numerical aperture value. The AR combiner adopted in the proposed system operates as a convex lens for right-handed circularly polarized light and operates as transparent glass for left-handed circularly polarized light. By merging this combiner with a transparent screen, such as diffuser-holographic optical elements (DHOEs), it is possible to make an AR near-eye display with a small form factor and a wide field of view. In addition, the proposed AR system compensates the chromatic aberration that occurs in PB phase lens by adopting three-layered DHOEs. The operating principle of the proposed system is covered, and its feasibility is verified with experiments and analysis.
Holography is one of the most prominent approaches to realize true-to-life reconstructions of objects. However, owing to the limited resolution of spatial light modulators compared to static ...holograms, reconstructed objects exhibit various coherent properties, such as content-dependent defocus blur and interference-induced noise. The coherent properties severely distort depth perception, the core of holographic displays to realize 3D scenes beyond 2D displays. Here, we propose a hologram that imitates defocus blur of incoherent light by engineering diffracted pattern of coherent light with adopting multi-plane holography, thereby offering real world-like defocus blur and photorealistic reconstruction. The proposed hologram is synthesized by optimizing a wave field to reconstruct numerous varifocal images after propagating the corresponding focal distances where the varifocal images are rendered using a physically-based renderer. Moreover, to reduce the computational costs associated with rendering and optimizing, we also demonstrate a network-based synthetic method that requires only an RGB-D image.
Abstract
An artificial muscle actuator resolves practical engineering problems in compact wearable devices, which are limited to conventional actuators such as electromagnetic actuators. Abstracting ...the fundamental advantages of an artificial muscle actuator provides a small-scale, high-power actuating system with a sensing capability for developing varifocal augmented reality glasses and naturally fit haptic gloves. Here, we design a shape memory alloy-based lightweight and high-power artificial muscle actuator, the so-called compliant amplified shape memory alloy actuator. Despite its light weight (0.22 g), the actuator has a high power density of 1.7 kW/kg, an actuation strain of 300% under 80 g of external payload. We show how the actuator enables image depth control and an immersive tactile response in the form of augmented reality glasses and two-way communication haptic gloves whose thin form factor and high power density can hardly be achieved by conventional actuators.
While recent research has shown that holographic displays can represent photorealistic 3D holograms in real time, the difficulty in acquiring high-quality real-world holograms has limited the ...realization of holographic streaming systems. Incoherent holographic cameras, which record holograms under daylight conditions, are suitable candidates for real-world acquisition, as they prevent the safety issues associated with the use of lasers; however, these cameras are hindered by severe noise due to the optical imperfections of such systems. In this work, we develop a deep learning-based incoherent holographic camera system that can deliver visually enhanced holograms in real time. A neural network filters the noise in the captured holograms, maintaining a complex-valued hologram format throughout the whole process. Enabled by the computational efficiency of the proposed filtering strategy, we demonstrate a holographic streaming system integrating a holographic camera and holographic display, with the aim of developing the ultimate holographic ecosystem of the future.
Lung cancer is currently the first leading cause of worldwide cancer deaths since the early stage of lung cancer detection is still a challenge. In lung diagnosis, nodules sometimes overlap with ribs ...and tissues on lung chest radiographic images, which are complex for doctors and radiologists. Dual-energy subtraction (DES) is a suitable solution to solve those issues. This article will develop an efficient iterative DES for lung chest radiographic images. Moreover, we propose an automatic algorithm for accurately determining bone and soft-tissue factors for subtraction. The proposed algorithm for determining the bone and soft-tissue factors is based on window/level ratio and radiographic histogram analysis. First, we take the image sampling from the original size 3072 × 3072 to 512 × 512 to reduce the processing time while achieving the bone and soft-tissue factors. Next, we compute the window/level ratio on the soft-tissue image. Finally, we determine the minimum value of the ratio to obtain the optimal soft-tissue and bone factors. Our experimental results show that our proposed algorithm achieves a minimized runtime of 200 ms, outperforming the GE algorithm’s time of 4 s. The runtime of our DES of 6.066 s is shorter than the Fujifilm algorithm of 10 s while visualizing nodules on soft-tissue images and obtaining a similar quality of the soft-tissue images compared with the other algorithms. The academic contributions include the proposed algorithm for determining bone and soft-tissue factors and the optimized iterative DES algorithm to minimize time and dose consumption.
•Lung cancer is regarded as the first leading cause of worldwide cancer deaths.•Dual-energy subtraction (DES) is a suitable solution to solve that issue.•We proposed an automatic algorithm for determining DES factors running in 200 ms.•We developed a simplified iterative DES running in 6.066 s.•Our DES optimized dose usage while visualizing nodules on soft-tissue images.
Lung cancer is currently the first leading cause of worldwide cancer deaths since the early stage of lung cancer detection is still a challenge. In lung diagnosis, nodules sometimes overlap with ribs ...and tissues on lung chest radiographic images, which are complex for doctors and radiologists. Dual-energy subtraction (DES) is a suitable solution to solve those issues. This article will develop an efficient iterative DES for lung chest radiographic images. Moreover, we propose an automatic algorithm for accurately determining bone and soft-tissue factors for subtraction. The proposed algorithm for determining the bone and soft-tissue factors is based on window/level ratio and radiographic histogram analysis. First, we take the image sampling from the original size 3072 × 3072 to 512 × 512 to reduce the processing time while achieving the bone and soft-tissue factors. Next, we compute the window/level ratio on the soft-tissue image. Finally, we determine the minimum value of the ratio to obtain the optimal soft-tissue and bone factors. Our experimental results show that our proposed algorithm achieves a minimized runtime of 200 ms, outperforming the GE algorithm’s time of 4 s. The runtime of our DES of 6.066 s is shorter than the Fujifilm algorithm of 10 s while visualizing nodules on soft-tissue images and obtaining a similar quality of the soft-tissue images compared with the other algorithms. The academic contributions include the proposed algorithm for determining bone and soft-tissue factors and the optimized iterative DES algorithm to minimize time and dose consumption.
Background
Diagnostic performance based on x‐ray breast imaging is subject to breast density. Although digital breast tomosynthesis (DBT) is reported to outperform conventional mammography in denser ...breasts, mass detection and malignancy characterization are often considered challenging yet.
Purpose
As an improved diagnostic solution to the dense breast cases, we propose a dual‐energy DBT imaging technique that enables breast compositional imaging at comparable scanning time and patient dose compared to the conventional single‐energy DBT.
Methods
The proposed dual‐energy DBT acquires projection data by alternating two different energy spectra. Then, we synthesize unmeasured projection data using a deep neural network that exploits the measured projection data and adjacent projection data obtained under the other x‐ray energy spectrum. For material decomposition, we estimate partial path lengths of an x‐ray through water, lipid, and protein from the measured and the synthesized projection data with the object thickness information. After material decomposition in the projection domain, we reconstruct material‐selective DBT images. The deep neural network is trained with the numerical breast phantoms. A pork meat phantom is scanned with a prototype dual‐energy DBT system to demonstrate the feasibility of the proposed imaging method.
Results
The developed deep neural network successfully synthesized missing projections. Material‐selective images reconstructed from the synthesized data present comparable compositional contrast of the cancerous masses compared with those from the fully measured data.
Conclusions
The proposed dual‐energy DBT scheme is expected to substantially contribute to enhancing mass malignancy detection accuracy particularly in dense breasts.
This paper describes a set of Performance Evaluation Tools (PETS) for document image zone segmentation and classification. The tools allow researchers and developers to evaluate, optimize and compare ...their algorithms by providing a variety of quantitative performance metrics. The evaluation of segmentation quality is based on the pixel-based overlaps between two sets of zones proposed by Randriamasy and Vincent. PETS extends the approach by providing a set of metrics for overlap analysis, RLE and polygonal representation of zones and introduces type-matching to evaluate zone classification. The software is available for research use.
Zero-point electromagnetic fields were first introduced to explain the origin of atomic spontaneous emission. Vacuum fluctuations associated with the zero-point energy in cavities are now utilized in ...quantum devices such as single-photon sources, quantum memories, switches and network nodes. Here we present three-dimensional (3D) imaging of vacuum fluctuations in a high-Q cavity based on the measurement of position-dependent emission of single atoms. Atomic position localization is achieved by using a nanoscale atomic beam aperture scannable in front of the cavity mode. The 3D structure of the cavity vacuum is reconstructed from the cavity output. The root mean squared amplitude of the vacuum field at the antinode is also measured to be 0.92±0.07 V cm(-1). The present work utilizing a single atom as a probe for sub-wavelength imaging demonstrates the utility of nanometre-scale technology in cavity quantum electrodynamics.
PDF
