The optical combiner is an important part of the optical see-through augmented reality display system. Waveguide is an appropriate solution due to its advantages such as light weight and compact structure. Because grating has replicability, it is a promising solution to the waveguide’s coupler for mass-production. In this paper, a grating coupler for waveguide is designed by using the rigorous coupled wave analysis (RCWA) to increase the accuracy of the simulation due to the critical dimension is similar to the wavelength. The uniformity of the diffraction efficiency is considered as an important parameter for a better displaying performance. The downhill algorithm is used to optimize the parameters of the grating. In order to obtain a large field of view, the thickness of the grating should be controlled carefully. Finally, two gratings are designed for the waveguide which can extend pupil horizontally. The displaying performance of the waveguide is simulated, and the grating couplers are fabricated by the nanoimprint lithography method. The characteristics of the gratings are tested such as transmittance and diffraction efficiency. The results show the proposed gratings can be utilized for waveguide’s coupler. It is believed that our results will give a better alternative for the augmented reality display system.
In compressive spectral imaging, three-dimensional spatio-spectral data cubes are recovered from two-dimensional projections. The quality of the compressive-sensing-based reconstruction is dependent on the coherence of the sensing matrix, which is determined by the system projection and the sparse prior. Studies on the optimization of the system projection, which mainly deals with the coded aperture, successfully decreases the coherence of the sensing matrix and improves the reconstruction quality. However, the optimization of the sparse prior considering the relationship between the system projection and the sparse prior remains a challenge. In this paper, we propose a gradient-descent-based sparse prior optimization algorithm for the coherence minimization of the sensing matrix in compressive spectral imaging. The Frobenius norm coherence is introduced as the cost function for the optimization, and the overcomplete dictionary is chosen as the sparse prior to solve the optimal sparse representation in the reconstruction as it provides higher degree of freedom for optimization compared to common orthogonal bases. The optimized dictionary effectively decreases the coherence of the sensing matrix from 0.880 to 0.604 and significantly improves the quantitative image quality metrics of the reconstructed hyperspectral images with the corresponding peak signal-to-noise ratio (PSNR) increased by 9 dB, the structural similarity (SSIM) above 0.98, and the spectrum angular mapper (SAM) below 0.1. Furthermore, the requirement of the sampling snapshots is reduced, which is shown by similar image quality metrics between the reconstructed hyperspectral images of only 1 snapshot with the optimized dictionary and of more than 5 snapshots with the non-optimized dictionary.
Metasurface optical elements such as metalenses have drawn great attentions for their capabilities of manipulating wavefront versatilely and miniaturizing traditional optical devices into ultrathin counterparts, and multi-functional metasurfaces such as bifocal metalenses have attracted tremendous interests due to their potential in system integration. In this paper, an approach to design polarization-dependent bifocal metalenses which are able to independently generate longitudinally or transversely bifocal spots under the incidence of circularly polarized light with arbitrary ellipticity is proposed and demonstrated by full-wave simulations. When the designed devices are illuminated with elliptically polarized lights at wavelength of 532 nm, both of the helicity-multiplexed bifocal spots appear simultaneously, and the relative intensity of both focal spots can be tuned in terms of the ellipticity of the polarization state. In addition, a polarization-independent metalens based on geometric phase modulation is illustrated and the focusing efficiency of it maintains stable ignoring the polarization state of the incident waves, which could be of vital importance in real applications. This design is of enormous potential of being applied in real compact optical systems such as imaging, display, microscopy, tomography, optical data storage and so on.
Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technology, which achieves high-resolution imaging with a wide filed-of-view by overcoming the limitation of the optical spatial-bandwidth-product (SBP). In the traditional FPM system, the aberration of the optical system is ignored, which may significantly degrade the reconstruction results. In this paper, we propose a novel FPM reconstruction method based on the forward neural network models with aberration correction, termed FNN-AC. Zernike polynomials are used to indicate the wavefront aberration in our method.Both the spectrum of the sample and coefficients of different Zernike modes are treated as the learnable weights in the trainable layers.By minimizing the loss function in the training process, the coefficients of different Zernike modes can be trained, which can be used to correct the aberration of the optical system. Simulation has been performed to verify the effectiveness of the FNN-AC.
To obtain surreal and richer visual experience, augmented reality (AR) technology has been widely used in various areas. As a popular solution of AR, display using computer generated hologram (CGH) is often accompanied by blurring which is caused by uncontrolled interference. In this paper, a modified algorithm based on double-phase hologram (DPH) algorithm is proposed to reduce speckle noise in holographic reconstruction. The macro-pixels in the original hologram are separated into multiple sub-holograms, and these sub-holograms are displayed alternately in high frequency, which reduces the speckle noise generated from the interference between adjacent macro-pixels. Meanwhile, the method is less time-consuming than the traditional Gerchberg-Saxton algorithm because no iteration is needed. The simulation and the optical experiment based on liquid crystal on silicon (LCoS) have been conducted, and the results confirm the feasibility of the proposed method to improve the image quality.
Convolutional neural networks (CNNs) is becoming a critical role for deep learning-based computer vision applications. Through CNN we can extract meaningful information out of massive sensor data. Vision applications use this information to analyze the mainstream trends of the data and take immediate action based on these trends. However, CNN's energy consumption and bandwidth limitations make it difficult for CNN network systems to deploy in mobile systems with stringent energy limits. In this paper, we explore the simulation and the hardware method of optical convolution for low power image processing, which is inspired by the bio-image sensor Angle sensitive pixel (ASP). This optical computation method may be used to substitute the first convolution layer of the CNN network due to its energy-saving features and speed of light processing time. We adopt two-layer of customized transmission grating to perform this optical convolution computing. By generating the Talbot effect, the two-layer grating structure can perform optical convolution computation using Gabor wavelet filters, which will cause zero electrical power. We demonstrate both simulation and experiment results for optical convolution through our algorithm and prototype system, the convolution results can extract different meaningful information about the original image, which is very similar to edge filtering. This optical operation will hopefully be used to replace the first convolution layer of CNN since it can effectively reduce both the consumption of the energy power and the performing time.
Artificial neural networks are computational models enlightened by biological neural networks, playing a significant role in image recognition, language translation and computer vision fields, etc. In this paper, we propose a fully optical neural network based on programmable nanophotonic processor (PNP) to realize digit recognition. The architecture includes 4 layers cascaded Mach–Zehnder interferometers (MZIs), which could theoretically execute matrix functions corresponding to a two-layer fully connected ANN with four inputs. We simulate cascaded MZIs and adjust phase shifters to match weight matrices calculated by ANN in computer beforehand. The accuracy of 4-class handwritten digits in ONN is 80.29% due to the compressed input data. The accuracy of 10-class digits could achieve 99.23% when the input node merely increases to 36. The results demonstrate the handwritten digits could be recognized effectively through PNP in ONN and the construction of PNP could be extended for more complex recognition systems.
We report an approach to enhance the resolution of the microscopy imaging by using the fourier ptychographic microscopy (FPM) method with a laser source and Spatial Light Modulator (SLM) to generate modulated sample illumination. The performance of the existed FPM system is limited by low illumination efficiency of the LED array. In our prototype setup, digital micromirror device (DMD) is introduced to replace the LED array as a reflective spatial light modulator and is placed at the front focal plane of the 4F system. A ring pattern sample illumination is generated by coding the micromirrors on the DMD, and converted to multi-angular illumination through the relay illumination system. A series of intensity sample images can be obtained by changing the size of the ring pattern and then used to reconstruct high resolution image through the ring pattern phase retrieval algorithm. Finally, our method is verified by an experiment using a resolution chart. The results also show that our method have higher reconstruction resolution and faster imaging speed.