Spectral imaging can obtain spectral information of target scenes and has wide applications many fields such as biomedical, military, agricultural, food safety. Coded aperture snapshot spectral imager (CASSI) proposed a snapshot spectral imaging system by employing compressed sensing theory. However, the use of the dispersion element leads to a deviation in the direction of light propagation, resulting in a complex system. This paper proposes a spectral imager that utilizes metasurface array instead of dispersion elements to encode the spectrum of the incident light and uses macro pixel segmentation method for spatial code. In terms of design, we designed nine nanofin structures with different longer and shorter axes. And these nine nanofin structures are formed as a macro pixel. Due to the fact that the design of the metasurface is not a C4 structure, it has a polarization conversion effect. When linearly polarized light is incident, the output will become two orthogonal polarized light with different spectral transmission. By utilizing the polarization image sensor on the detection end, more information can be obtained in one snapshot to increase the accuracy of recovery.
Compared with array detectors (such as CCD or CMOS), single pixel detectors have potential in invisible band and weak light applications to broaden the spectrum of spectral imaging. Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI). These CNN-based methods achieve impressive restoration performance while showing limitations in capturing the long-range dependencies and self-similarity prior. To cope with this problem, we propose a novel spectral-attention transformer(SAT-net) method for single-pixel multispectral reconstruction. In addition, we introduce total variation (TV) to maintain the smooth structure of HSI. The experimental results of simulation and real data show that the proposed SAT-net is superior to other traditional algorithms based on compressive sensing(CS) methods.
The remarkable ability of metasurfaces to manipulate wavefronts in a versatile manner, coupled with their compact and ultrathin configuration, has garnered significant interest. These characteristics make metasurfaces ideally suited for fulfilling the demands of compact computational imaging systems. In this research endeavor, we present a comprehensive metasurface inverse design model, which operates in an end-to-end method, within a joint optimization framework for computational imaging. To exemplify its efficacy, we demonstrate the application of this model using silicon meta-atoms, showcasing its potential for broadband near-infrared imaging. This work contributes to the advancement of computational imaging techniques, providing a promising avenue for future developments in the field.
Spectral imaging can simultaneously capture the spatial and spectral data of target objects, and provide multidimensional technique for analysis and recognition in many fields, including remote sensing, agriculture and biomedicine. To increase the efficiency of data acquisition, compressed sensing (CS) methods have been introduced into spectral imaging systems, especially single-pixel spectral imaging systems. However, the traditional CS single-pixel spectral imaging system is not stable enough and has complex structure, so we propose a novel macro-pixel segmentation method based on broadband spectrum multispectral filter arrays. In this system, structural illumination and broadband multispectral filter arrays are used to generate spatial modulation and spectral modulation respectively, to modulate 3-D data cube of a scene. The macro-pixel units of the patterns are aimed to capture spatial information, and the sub regions in each macropixel unit are aimed to capture spectral information. The filter arrays can be designed and processed according to specific requirements. By changing the number of sub regions of each macro-pixel unit and the transmittance curve of each sub region, the imaging spectrum can be flexibly changed, and the anti-noise performance of the system can be greatly improved. CS algorithm is used to effectively recover 3-D data cube from one-dimensional signal collected by single-pixel detector. Compared with array detectors (e.g. CCD or CMOS), single-pixel detectors have potential in invisible band and low light applications. Besides, without mechanical or dispersive structure, our strategy has great advantages in miniaturization and integration of spectral imaging equipment.
Metasurfaces and metalenses have drawn great attentions since they can manipulate wavefront versatilely with a miniaturized and ultrathin configuration. Here we propose and numerically verify a tunable bifocal metalens with two continuous-zoom foci. This device utilizes two cascaded and circle layers of metasurfaces with different phase distributions for incidences of opposite helicities imparted on each layer by the combination of geometric phase and propagation phase. By relative rotation of both layers, focal lengths of both foci can be tuned continuously with the zoom range for each focus designed deliberately, and the relative intensity of both foci can be adjusted by changing the polarization state of incidence. The proposed device is anticipated to be applied in polarization imaging, depth estimation, multi-plane imaging, optical data storage, and so on.
Spectral imaging can capture 2D spatial and 1D spectral information of target scene. This 3D data has important applications in wide range of fields, including military, medicine, and agronomy. Spectral imager combined with compressive sensing can significantly reduce the amount of detection data and detection time, so it has been widely studied. Coded aperture snapshot spectral imager (CASSI) is the first spectral imager that combines compression sensing theory. However, it uses dispersion prism, which makes the system very complex, to encode the incident light. In this paper, a spectral imager using dual spectral filter array to encode the incident light is proposed, and it avoids the use of dispersion elements. Dual spectral filter array is divided into a series of macro pixels, which is composed of 3×3 filters. The macro pixel of the first filter is composed of three low-pass filters, three band-pass filters, and three high pass filters. The macro pixel of the second filter is composed of 9 filters with different transmission curves to archive the coding. In addition, we add a beam splitter in front of the objective lens to divide the optical path into two paths, one as the detection arm for spectral imaging, and the other as the reference arm to improve the recovery effect.
Metasurfaces, composed of two-dimensional arrays of subwavelength optical scatterers, are regarded as powerful substitutes to conventional diffractive and refractive optics. In addition, metasurfaces with powerful wavefront manipulation capabilities can steer the phase, amplitude, and polarization of light, which provides the potential to joint optimization with algorithms by encoding and decoding the light fields. In this paper, we propose an end-to-end computational imaging system which is joint optimized of metaoptics and neural networks based on the designed initial phase. We construct the forward model of the unit cell to the optical response and the inverse mapping of the optical response to the unit cell for the differentiable front-end metaoptics. Based on the appropriate initial phase, the calculation of the framework would converge faster, and the proposed system will promote the further development of metaoptics and computational imaging.
As novel planar structures, the metasurfaces exhibit the unprecedented capability to manipulate the amplitude, phase, and polarization of electromagnetic waves. Therefore, metasurface is designed to apply to metalens, holography, nanoprinting display, encryption, and so on. It is very interesting and meaningful work to integrate bifocal metalens and nanoprinting images into a single metasurface. A method is proposed to combine propagation phase and geometric phase, as well as Malus's law to realize the function of the bifocal metalens and clear nanoprinting display in the near field which can be observed at a certain polarization. This original design expands the functional integration of metasurface and improves applications in image displays, optical storage, augmented reality, virtual reality, and many other related fields.
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.
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.
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.
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.
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.
Spectral confocal technology is an important three-dimensional measurement technology with high accuracy and non-contact; however, traditional spectral confocal system usually consists of prisons and several lens whose volume and weight is enormous and heavy, besides, due to the chromatic aberration characteristics of ordinary optical lenses, it is difficult to perfectly focus light in a wide bandwidth. Meta-surfaces are expected to realize the miniaturization of conventional optical element due to its superb abilities of controlling phase and amplitude of wavefront of incident at subwavelength scale, and in this paper, an efficient spectral confocal meta-lens (ESCM) working in the near infrared spectrum (1300nm-2000nm) is proposed and numerically demonstrated. ESCM can focus incident light at different focal lengths from 16.7 to 24.5μm along a perpendicular off-axis focal plane with NA varying from 0.385 to 0.530. The meta-lens consists of a group of Si nanofins providing high polarization conversion efficiency lager than 50%, and the phase required for focusing incident light is well rebuilt by the resonant phase which is proportional to the frequency and the wavelength-independent geometric phase, PB phase. Such dispersive components can also be used in implements requiring dispersive device such as spectrometers.
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.
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.
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.
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