Research on the localized lightfield features of metallic nano-cone-tip optical antenna via investigating near-field lightwave and correlated net-charge distribution.

In this paper, the near-field lightwave characteristics of an arrayed silicon nano-cone-tip optical antenna (NOA) covered by a common metal film, which can be viewed as a featured quasi quantum dot (QD), are carefully investigated. A dipole net-charge distribution closely correlated with the surface lightwaves excited over the antennas by incident lasers with a central wavelength of 633 nm, is clearly observed. An obvious Coulomb-like blockade from the apex apparently influencing the net-charge converging over the surface of NOA, is verified, which can also be predicted by the simulations according to surface standing waves across the apex node. The antinodes of the surface net-charge instantaneous distribution are already pushed away from the normal location owing to the apex Coulomb-like blockade, so as to present a distorted waveform different from traditional standing wave modes. The tip proximity effect leading to a relatively weak net-charge converging over surrounding planar facet and adjacent NOAs, is also discovered.

Electrically addressed focal stack plenoptic camera based on a liquid-crystal microlens array for all-in-focus imaging.

Focal stack cameras are capable of capturing a stack of images focused at different spatial distance, which can be further integrated to present a depth of field (DoF) effect beyond the range restriction of conventional camera's optics. To date, all of the proposed focal stack cameras are essentially 2D imaging architecture to shape 2D focal stacks with several selected focal lengths corresponding to limited objective distance range. In this paper, a new type of electrically addressed focal stack plenoptic camera (EAFSPC) based on a functional liquid-crystal microlens array for all-in-focus imaging is proposed. As a 3D focal stack camera, a sequence of raw light-field images can be rapidly manipulated through rapidly shaping a 3D focal stack. The electrically addressed focal stack strategy relies on the electric tuning of the focal length of the liquid-crystal microlens array by efficiently selecting or adjusting or jumping the signal voltage applied over the microlenses. An algorithm based on the Laplacian operator is utilized to composite the electrically addressed focal stack leading to raw light-field images with an extended DoF and then the all-in-focus refocused images. The proposed strategy does not require any macroscopic movement of the optical apparatus, so as to thoroughly avoid the registration of different image sequence. Experiments demonstrate that the DoF of the refocused images can be significantly extended into the entire tomography depth of the EAFSPC, which means a significant step for an all-in-focus imaging based on the electrically controlled 3D focal stack. Moreover, the proposed approach also establishes a high correlation between the voltage signal and the depth of in-focus plane, so as to construct a technical basis for a new type of 3D light-field imaging with an obvious intelligent feature.

Orthogonal separation of arbitrary vector beams from non-polarized light waves based on a patterned liquid-crystal photo-alignment.

An effective method for orthogonally separating arbitrary vector polarized beams from non-polarized incident light waves is proposed in this Letter. A tunable patterned spatial distribution of liquid-crystal (LC) molecules can be effectively constructed based on both the initial photo-alignment and the electrically controlled birefringence of nematic LC materials. The LC photo-alignment over a smooth surface without any common nano-grooves leads to a highly efficient light-wave transformation by inducing a desired initial arrangement of LC directors and then acquiring extraordinary light waves with the needed, or even arbitrary, spatial polarization. The vector polarized beams can be highly converged according to a microhole-patterned electrode and a gradient refractive index distribution of the LC layer, which is driven and adjusted by an applied signal voltage. Due to the intrinsic polarization sensitivity of nematic LC materials, the formed gradient refractive index appearance only corresponds to extraordinary light waves. The proposed approach provides a way to achieve the orthogonal separation of arbitrary vector beams from non-polarized light waves. Moreover, it can be further utilized to generate and obtain arbitrary vector beams, as well as to perform adaptive light-beam convergence or even the focusing of arbitrary vector beams, which is expected to advance the development of vector beam generation and manipulation, thereby stimulating potential applications.

The mr-MDA: An Invariant to Shifting, Scaling, and Rotating Variance for 3D Object Recognition Using Diffractive Deep Neural Network.

The diffractive deep neural network (D2NN) can efficiently accomplish 2D object recognition based on rapid optical manipulation. Moreover, the multiple-view D2NN array (MDA) possesses the obvious advantage of being able to effectively achieve 3D object classification. At present, 3D target recognition should be performed in a high-speed and dynamic way. It should be invariant to the typical shifting, scaling, and rotating variance of targets in relatively complicated circumstances, which remains a shortcoming of optical neural network architectures. In order to efficiently recognize 3D targets based on the developed D2NN, a more robust MDA (mr-MDA) is proposed in this paper. Through utilizing a new training strategy to tackle several random disturbances introduced into the optical neural network system, a trained mr-MDA model constructed by us was numerically verified, demonstrating that the training strategy is able to dynamically recognize 3D objects in a relatively stable way.

Lightwave nano-converging enhancement by an arrayed optical antenna based on metallic nano-cone-tips for CMOS imaging detection.

A kind of gold-coated glass nano-cone-tips (GGNCTs) is developed as an arrayed optical antenna for highly receiving and converging incident lightwaves. A local light field enhancement factor (LFEF) of ~ 2 × 104 and maximum light absorption of ~ 98% can be achieved. The near-field lightwave measurements at the wavelength of 633 nm show that the surface net charges over a single GGNCT make a typical dipole oscillation and the energy transmits along the wave vector orientation, thus leading to a strong local light field enhancement. An effective detection method by near-field coupling an arrayed GGNCT and complementary metal-oxide-semiconductor (CMOS) sensor for highly efficient imaging detection is proposed. The lightwave detection at several wavelengths, including typical 473 nm, 532 nm, 671 nm, and 980 nm, shows a notable characteristic that a better capability of the net charge distribution adjusting and localized aggregating can be obtained at the absorption peak of the GGNCT developed and a stronger signal detection achieved. The research lays a foundation for further developing a light detector with an ideal optoelectronic sensitivity and broad spectral suitability, which is based on integrating GGNCTs as an arrayed optical antenna with common sensors.

Spatial separation of azimuthally and radially polarized beams from non-polarized light waves based on the electrically controlled birefringence effect.

Based on the electrically controlled birefringence effect in liquid crystal materials, an effective method for spatially separating azimuthally and radially polarized beams from non-polarized incident light waves is proposed. The radially polarized beam was highly converged by using a microhole-patterned electrode and a planar photo-alignment layer to shape the initial liquid-crystal radial alignment and a gradient refractive index distribution with central axial symmetry after applying a voltage signal. Due to the intrinsic polarization sensitivity of nematic liquid-crystal materials, the shaped gradient refractive index only applies to extraordinary light waves, which then converge into a spot. Thus, the azimuthally and radially polarized beams are effectively separated. The proposed method demonstrates some advantages, such as low cost, miniaturization, and easy fabrication and integration with other functional devices. Thanks to the wideband electrically controlled birefringence of liquid-crystal materials, this light-wave manipulation to spatially separate azimuthally and radially polarized beams can also be performed over a wide wavelength range.

All-In-Focus Polarimetric Imaging Based on an Integrated Plenoptic Camera with a Key Electrically Tunable LC Device.

In this paper, a prototyped plenoptic camera based on a key electrically tunable liquid-crystal (LC) device for all-in-focus polarimetric imaging is proposed. By using computer numerical control machining and 3D printing, the proposed imaging architecture can be integrated into a hand-held prototyped plenoptic camera so as to greatly improve the applicability for outdoor imaging measurements. Compared with previous square-period liquid-crystal microlens arrays (LCMLA), the utilized hexagonal-period LCMLA has remarkably increased the light utilization rate by ~15%. Experiments demonstrate that the proposed imaging approach can simultaneously realize both the plenoptic and polarimetric imaging without any macroscopic moving parts. With the depth-based rendering method, both the all-in-focus images and the all-in-focus degree of linear polarization (DoLP) images can be obtained efficiently. Due to the large depth-of-field advantage of plenoptic cameras, the proposed camera enables polarimetric imaging in a larger depth range than conventional 2D polarimetric cameras. Currently, the raw light field images with three polarization states including I0 and I60 and I120 can be captured by the proposed imaging architecture, with a switching time of several tens of milliseconds. Some local patterns which are selected as interested target features can be effectively suppressed or obviously enhanced by switching the polarization state mentioned. According to experiments, the visibility in scattering medium can also be apparently improved. It can be expected that the proposed polarimetric imaging approach will exhibit an excellent development potential.

Broad-spectrum diffractive network via ensemble learning.

We propose a broad-spectrum diffractive deep neural network (BS-D2NN) framework, which incorporates multiwavelength channels of input lightfields and performs a parallel phase-only modulation using a layered passive mask architecture. A complementary multichannel base learner cluster is formed in a homogeneous ensemble framework based on the diffractive dispersion during lightwave modulation. In addition, both the optical sum operation and the hybrid (optical-electronic) maxout operation are performed for motivating the BS-D2NN to learn and construct a mapping between input lightfields and truth labels under heterochromatic ambient lighting. The BS-D2NN can be trained using deep learning algorithms to perform a kind of wavelength-insensitive high-accuracy object classification.

Multiple-view D2NNs array: realizing robust 3D object recognition.

As an optical-based classifier of the physical neural network, the independent diffractive deep neural network (D2NN) can be utilized to learn the single-view spatial featured mapping between the input lightfields and the truth labels by preprocessing a large number of training samples. However, it is still not enough to approach or even reach a satisfactory classification accuracy on three-dimensional (3D) targets owing to already losing lots of effective lightfield information on other view fields. This Letter presents a multiple-view D2NNs array (MDA) scheme that provides a significant inference improvement compared with individual D2NN or Res-D2NN by constructing a different complementary mechanism and then merging all base learners of distinct views on an electronic computer. Furthermore, a robust multiple-view D2NNs array (r-MDA) framework is demonstrated to resist the redundant spatial features of invalid lightfields due to severe optical disturbances.

Robust light beam diffractive shaping based on a kind of compact all-optical neural network.

A kind of compact all-optical learning-based neural network has been constructed and characterized for efficiently performing a robust layered diffractive shaping of laser beams. The data-driven control lightwave strategy demonstrates some particular advantages such as smart or intelligent light beam manipulation, optical data statistical inference and incident beam generalization. Based on the proposed method, several typical aberrated light fields can be effectively modulated into the desired fashion including the featured flat-top beams, an arrayed sub-beam arrangement and complex annular fringes compared with conventional GS-based DOEs. An actual THz laser is utilized to evaluate the effectiveness of the method developed.

Anti-noise diffractive neural network for constructing an intelligent imaging detector array.

To develop an intelligent imaging detector array, a diffractive neural network with strong robustness based on the Weight-Noise-Injection training is proposed. According to layered diffractive transformation under existing several errors, an accurate and fast object classification can be achieved. The fact that the mapping between the input image and the label in Weight-Noise-Injection training mode can be learned, means that the prediction of the optical network being insensitive to disturbances so as to improve its noise resistance remarkably. By comparing the accuracy under different noise conditions, it is verified that the proposed model can exhibit a higher accuracy.

Electrically Controlled Liquid Crystal Microlens Array Based on Single-Crystal Graphene Coupling Alignment for Plenoptic Imaging.

As a unique electric-optics material, liquid crystals (LCs) have been used in various light-control applications. In LC-based light-control devices, the structural alignment of LC molecules is of great significance. Generally, additional alignment layers are required for LC lens and microlens, such as rubbed polyimide (PI) layers or photoalignment layers. In this paper, an electrically controlled liquid crystal microlens array (EC-LCMLA) based on single-crystal graphene (SCG) coupling alignment is proposed. A monolayer SCG with high conductivity and initial anchoring of LC molecules was used as a functional electrode, thus no additional alignment layer is needed, which effectively simplifies the basic structure and process flow of conventional LCMLA. Experiments indicated that a uniform LC alignment can be acquired in the EC-LCMLA cell by the SCG coupling alignment effect. The common optical properties including focal lengths and point spread function (PSF) were measured experimentally. Experiments demonstrated that the proposed EC-LCMLA has good focusing performance in the visible to near-infrared range. Moreover, the plenoptic imaging in Galilean mode was achieved by integrating the proposed EC-LCMLA with photodetectors. Digital refocusing was performed to obtain a rendering image of the target.

Generating Convergent Laguerre-Gaussian Beams Based on an Arrayed Convex Spiral Phaser Fabricated by 3D Printing.

A convex spiral phaser array (CSPA) is designed and fabricated to generate typical convergent Laguerre-Gaussian (LG) beams. A type of 3D printing technology based on the two-photon absorption effect is used to make the CSPAs with different featured sizes, which present a structural integrity and fabricating accuracy of ~200 nm according to the surface topography measurements. The light field vortex characteristics of the CSPAs are evaluated through illuminating them by lasers with different central wavelength such as 450 nm, 530 nm and 650 nm. It should be noted that the arrayed light fields out from the CSPA are all changed from a clockwise vortex orientation to a circular distribution at the focal plane and then a counterclockwise vortex orientation. The circular light field is distributed 380-400 µm away from the CSPA, which is close to the 370 µm of the focal plane design. The convergent LG beams can be effectively shaped by the CASPs produced.

Depth-of-Field-Extended Plenoptic Camera Based on Tunable Multi-Focus Liquid-Crystal Microlens Array.

Plenoptic cameras have received a wide range of research interest because it can record the 4D plenoptic function or radiance including the radiation power and ray direction. One of its important applications is digital refocusing, which can obtain 2D images focused at different depths. To achieve digital refocusing in a wide range, a large depth of field (DOF) is needed, but there are fundamental optical limitations to this. In this paper, we proposed a plenoptic camera with an extended DOF by integrating a main lens, a tunable multi-focus liquid-crystal microlens array (TMF-LCMLA), and a complementary metal oxide semiconductor (CMOS) sensor together. The TMF-LCMLA was fabricated by traditional photolithography and standard microelectronic techniques, and its optical characteristics including interference patterns, focal lengths, and point spread functions (PSFs) were experimentally analyzed. Experiments demonstrated that the proposed plenoptic camera has a wider range of digital refocusing compared to the plenoptic camera based on a conventional liquid-crystal microlens array (LCMLA) with only one corresponding focal length at a certain voltage, which is equivalent to the extension of DOF. In addition, it also has a 2D/3D switchable function, which is not available with conventional plenoptic cameras.

Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement.

We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN). Moreover, the training data is cropped into small overlapped patches to expand the training samples for the DNN. The performance of the proposed approach is verified by experimental projection fringe patterns with applications in dynamic fringe projection 3D measurement.