Coded aperture snapshot spectral imaging (CASSI) is a technique that can capture 3D hyperspectral images (HSIs) of scenes in a single shot. However, the quality of the reconstructed HSIs is affected by various optical aberrations and system noise. Existing deep learning methods for HSI reconstruction do not consider these degradation patterns and thus lack generalization ability to real CASSI data. In this paper, we propose a practical method to recover high-quality HSIs from low-quality CASSI data. We use a spectral imaging simulation to generate authentic training data that reflects the optical aberrations of the CASSI system. We then train a generative network on this data to remove blur and chromatic aberrations from the CASSI measurements. Our experiments show that our method can effectively improve the quality of the reconstructed HSIs and can be easily applied to real CASSI systems.
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.
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.
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.
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 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.
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.
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