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.
Traditional hyperspectral imagers rely on scanning either the spectral or spatial dimension of the hyperspectral cube with spectral filters or line-scanning which can be time consuming and generally require precise moving parts, increasing the complexity. More recently, snapshot techniques have emerged, enabling capture of the full hyperspectral datacube in a single shot. However, some types of these snapshot system are bulky and complicated, which is difficult to apply to the real world. Therefore, this paper proposes a compact snapshot hyperspectral imaging system based on compressive theory, which consists of the imaging lens, light splitter, micro lens array, a metasurface-covered sensor and an RGB camera. The light of the object first passes through the imaging lens, and then a splitter divides the light equally into two directions. The light in one direction pass through the microlens array and then the light modulation is achieved by using a metasurface on the imaging sensor. Meanwhile, the light in another direction is received directly by an RGB camera. This system has the following advantages: first, the metasurface supercell can be well designed and arranged to optimize the transfer matrix of the system; second, the microlens array guarantee that the light incident on the metasurface at a small angle, which eliminate the transmittance error introduced by the incidence angle; third, the RGB camera is able to provide side information and help to ease the reconstruction.
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.
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.
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.
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.
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