Computational spectral imaging is an emerging interdisciplinary technique with extensive applications. Current systems based on meta-surfaces use broad-band nanophotonic encoders for spectral encoding, which requires a high sampling rate to achieve ideal spectral resolution. In this study, we propose a high-resolution computational spectral imaging system based on quasi-bound states in the continuum (quasi-BIC) achieved by breaking the C2 symmetry of a metarectangle structure. Compared to broad-band nanophotonic encoders, quasi-BIC features a high-quality factor (Q-factor), with resonance peaks that can be shifted across a wide wavelength range by adjusting a scale factor S, and the width of these peaks can be controlled by rotating orientation angles. This quasi-BIC spectral encoder enhances the accuracy of computational reconstruction and improves spectral resolution, achieving an average peak-signal-to-noise ratio (PSNR) of 35.12 dB and an average spectral angle mapper (SAM) of 0.0616 at a sampling rate of 29%. This advancement paves the way for the development of compact and high-resolution spectral imaging devices.
Specular highlight in images is detrimental to accuracy in object recognition tasks. The prior model-based methods for single image highlight removal (SHIR) are limited in images with large highlight regions or achromatic regions, and recent learning-based methods do not perform well due to lack of proper datasets for training either. A network for SHIR is proposed, which is trained with losses that utilize image intrinsic features and can reconstruct a smooth and natural specular-free image from a single input highlight image. Dichromatic reflection model is used to compute the pseudo specular-free image for providing complementary information for the network. A real-world dataset with highlight images and the corresponding ground-truth specular-free images is collected for network training and quantitative evaluation. The proposed network is validated by comprehensive quantitative experiments and outperforms state-of-the-art highlight removal approaches in structural similarity and peak signal-to-noise ratio. Experimental results also show that the network could improve the recognition performance in applications of computer vision. Our source code is available at https://github.com/coach-wang/SIHRNet.
Coded aperture snap shot spectral imager (CASSI) is a potential method to get hyperspectral images. One of the latest designs of CASSI is a dual-camera design, which adds a grayscale camera to capture the same scene. In this paper, an improved method based on two-step iterative shrinkage thresholding algorithms (TwIST) is proposed to utilize the images containing the information of the structure of the objects from the grayscale camera more efficiently. The information come from the auxiliary camera and the CASSI detector is used to construct an estimated 3D hyperspectral data. Then we use TwIST and TV regularization to reconstruct the residual image based on the residual data. The final reconstructed hyperspectral image equals the sum of the estimated image and the reconstruct residual image. This method ensures that the result is more similar to the structure of the original image. The simulation results show that our method improves the image quality of the reconstructed hyperspectral images for all the data we have tried. The simulation results show that our method improves the image quality of the reconstructed hyperspectral images and use less run time compared to the original method. The corresponding peak signal-to-noise ratio (PSNR) is increased by 8.99 dB. The structural similarity (SSIM) is increased by 0.0757. The spectrum angular mapper (SAM) is reduced by 0.1987.
Spectral imaging can capture both spatial and spectral data of a scene, providing an efficient technique for analysis and identification. To improve the efficiency of data acquisition, compressive sensing (CS) methods have been introduced into spectral imaging systems. In this work, we propose a novel macropixel segmentation method to realize effective and non-mechanical single-pixel multispectral imaging. A series of macropixel-based patterns are designed to modulate data cube of target object. Spatial light modulator (SLM) and multispectral filter array are utilized to generate such patterns. CS algorithm is used to recover data cube from 1-D signal acquired by a single-pixel detector. Alignment of binary patterns with the subareas of macropixel filter array is conducted in the experimental set-up. Without mechanical or dispersive structure, the proposed method holds great potential in miniaturization and integration of spectral imaging devices.
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.
KEYWORDS: 3D displays, Far-field diffraction, Holography, 3D modeling, Computer generated holography, Holograms, Spatial light modulators, Near field diffraction, Process modeling
A simple yet effective method to realize holographic three-dimensional (3D) display by shifted Fraunhofer diffraction has been presented in this paper. After a 3D object is divided into a set of layers in axial direction, these layers are calculated into corresponding sub-holograms by Fraunhofer diffraction. The hologram uploaded on SLM consists of sub-holograms in a tiling approach. Both simulations and experiments are carried out to verify the feasibility of shifted Fraunhofer diffraction. Detailed analysis of computational cost has also been carried out, and the comparison between shifted Fresnel diffraction and shifted Fraunhofer diffraction in the proposed method has been analyzed. The experimental results demonstrate that our method can reconstruct multi-plane 3D object with continuous depth map and the process of 3D modeling is simple, that is the computational complexity is accordingly reduced.
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.
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.
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.
Metasurfaces are expected to realize the miniaturization of conventional refractive optics into planar structures; however, they suffer from large chromatic aberration due to the high phase dispersion of their subwavelength building blocks, limiting their real applications in imaging and displaying systems. In this paper, a high-efficient broadband achromatic metasurface (HBAM) is designed and numerically demonstrated to suppress the chromatic aberration in the continuous visible spectrum. The HBAM consists of TiO2 nanofins as the metasurface building blocks (MBBs) on a layer of glass as the substrate, providing a broadband response and high polarization conversion efficiency for circularly polarized incidences in the desired bandwidth. The phase profile of the metasurface can be separated into two parts: the wavelength -independent basic phase distribution represented by the Pancharatnam-Berry (PB) phase, depending only on the orientations of the MBBs, and the wavelength-dependent phase dispersion part. The HBAM applies resonance tuning for compensating the phase dispersion, and further eliminates the chromatic aberration by integrating the phase compensation into the PB phase manipulation. The parameters of the HBAM structures are optimized in finite difference time domain (FDTD) simulation for enhancing the efficiency and achromatic focusing performance. Using this approach, this HBAM is capable of focusing light of wavelengths covering the entire visible spectrum (from 400 nm to 700 nm) at the same focal plane with the spot sizes close to the diffraction limit. The minimum polarization conversion efficiency of most designed MBBS in such spectrum is above 20%. This design could be viable for various practical applications such as cameras and wearable optics.
Computational imaging spectrometry provides spatial-spectral information of objects. This technology has been applied in biomedical imaging, ocean monitoring, military and geographical object identification, etc. Via compressive sensing with coded apertures, 3D spatial-spectral data cube of hyperspectral image is compressed into 2D data array to alleviate the problems due to huge amounts of data. In this paper, a 3D convolutional neural network (3D CNN) is proposed for reconstruction of compressively sensed (CS) multispectral image. This network takes the 2D compressed data as the input and gives an intermediate output, which has identical size with the original 3D data. Then a general image denoiser is applied on it to obtain the final reconstruction result. The network with one fully connected layer, six 3D convolutional layers is trained with a standard hyperspectral image dataset. Though the compression rate is extremely high (16:1), this network performs well both in spectral reconstruction, demonstrated with single point spectrum, and in quantitative comparison with original data, in terms of peak signal to noise ratio (PSNR). Compared with state-of-the-art iterative reconstruction methods e.g. two-step iterative shrinkage/thresholding (TwIST), this network features high speed reconstruction and low spectral dispersion, which potentially guarantees more accurate identification of objects.
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.
A novel method is proposed in this paper to accurately reconstruct the three-dimensional scenes by using a passive single-shot exposure with a lenslet light field camera. This method has better performance of 3D scenes reconstruction with both defocus and disparity depth cues captured by light field camera. First, the light field data is used to refocus and shift viewpoints to get a focal stack and multi-view images. In refocusing procedure, the phase shift theorem in the Fourier domain is first introduced to substitute shift in spatial domain, and sharper focal stacks can be obtained with less blurriness. Thus, 3D scenes can be reconstructed more accurately. Next, through multi-view images, disparity depth cues are obtained by performing correspondence measure. Then, the focal stack is used to compute defocus depth cues by focus measure based on gray variance. Finally, the focus cost is built to integrate both defocus and disparity depth cues, and the accurate depth map is estimated by using Graph Cuts based on the focus cost. Using this accurate depth map and all-in-focus image, the 3D structure in real world are accurately reconstructed. Our method is verified by a number of synthetic and real-world examples captured with a dense camera array and a Lytro light field camera.
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