Compressive spectral imaging (CSI) senses the spatio-spectral information of a scene by measuring 2D coded projections on a focal plane array. A ℓ 1 -norm-based optimization algorithm is then used to ...recover the underlying discretized spectral image. The coded aperture snapshot spectral imager (CASSI) is an architecture realizing CSI where the reconstruction image quality relies on the design of a 2D set of binary coded apertures which block-unblock the light from the scene. This paper extends the compressive capabilities of CASSI by replacing the traditional blocking-unblocking coded apertures by a set of colored coded apertures. The colored coded apertures are optimized such that the number of projections is minimized while the quality of reconstruction is maximized. The optimal design of the colored coded apertures aims to better satisfy the restricted isometry property in CASSI. The optimal designs are compared with random colored coded aperture patterns and with the traditional blocking-unblocking coded apertures. Extensive simulations show the improvement in reconstruction PSNR attained by the optimal colored coded apertures designs.
Inverse lithography technology (ILT) is extensively used to compensate image distortion in optical lithography systems by pre-warping the photomask at the pixel scale. However, computational ...complexity is always a central challenge of ILT due to the big throughput of data volume. This paper proposes a dual-channel model-driven deep learning (DMDL) method to overcome the computational burden, while break through the limit of image fidelity over traditional ILT algorithms. The architecture of DMDL network is not inherited from conventional deep learning, but derived from the inverse optimization model under a gradient-based ILT framework. A dual-channel structure is introduced to extend the capacity of the DMDL network, which allows to simultaneously modify the mask contour and insert sub-resolution assist features to further improve the lithography image fidelity. An unsupervised training strategy based on auto-decoder is developed to avoid the time-consuming labelling process. The superiority of DMDL over the state-of-the-art ILT method is verified in both of the computational efficiency and image fidelity obtained on the semiconductor wafer.
A compressive spectral-temporal imaging system is reported. A multi-spectral light-emitting diode array is used for target illumination and spectral modulation, while a digital micro-mirror device ...(DMD) encodes the spatial and temporal frames. Several encoded video frames are captured in a snapshot of an integrating focal plane array (FPA). A high-frame-rate spectral video is reconstructed from the sequence of compressed measurements captured by the grayscale low-frame-rate camera. The imaging system is optimized through the design of the DMD patterns based on the forward model. Laboratory implementation is conducted to validate the performance of the proposed imaging system. We experimentally demonstrate the video acquisition with eight spectral bands and six temporal frames per FPA snapshot, and thus a 256 × 256 × 8 × 6 4D cube is reconstructed from a single 2D measurement.
A compressive imaging spectropolarimeter is proposed in this paper, capable of simultaneously acquiring full polarization, spatial and spectral information of the object scene. The spectral and ...polarization information is modulated through a combination of high-order retarders, a dispersion prism and a polarizer filter wheel. Using a random coded aperture, compressive sensing is applied to eliminate the channel crosstalk and resolution limitation of traditional channeled spectropolarimeters. The forward sensing model and inverse problem are developed. Computer simulation results are reported, followed by experimental demonstrations.
Compressive sensing is a powerful sensing and reconstruction framework for recovering high dimensional signals with only a handful of observations and for spectral imaging, compressive sensing offers ...a novel method of multispectral imaging. Specifically, the coded aperture snapshot spectral imager (CASSI) system has been demonstrated to produce multi-spectral data cubes color images from a single snapshot taken by a monochrome image sensor. In this paper, we expand the theoretical framework of CASSI to include the spectral sensitivity of the image sensor pixels to account for color and then investigate the impact on image quality using either a traditional color image sensor that spatially multiplexes red, green, and blue light filters or a novel Foveon image sensor which stacks red, green, and blue pixels on top of one another.
Spectral computed tomography (CT) relies on the spectral dependence of X-ray attenuation coefficients to separate projection measurements into more than two energy bins. Such data can be used to ...unveil tomographic material characterization - key in national security and medical imaging. This paper explores a radical departure from conventional methods used in spectral imaging. It relies on K-edge coded apertures to create spatially and spectrally coded, lower-dose, X-ray bundles that interrogate specific voxels of the object. The new approach referred to as compressive spectral X-ray imaging (CSXI) uses low-cost standard X-ray integrating detectors and acquires compressive measurements, which enable the reconstruction of energy binned images from fewer measurements. Various spectral and spatial coding strategies for structured illumination are explored. Subsampling in CSXI is accomplished by either view angle spectral subsampling, spatial subsampling enabled by block-unblock coded apertures placed at the source or detector side, or both. The careful design of subsampling strategies, spectral filters, coded apertures, and their placement, are shown to be critical for the quality of tomographic image reconstruction. The forward imaging model of CSXI, which is a non-linear ill-posed problem, is analyzed and a multi-stage algorithm is developed to address the estimation of the energy binned sinograms from the integrating detector measurements. Then, an Alternating Direction Method of Multipliers (ADMM) is used to solve a joint sparse and low-rank optimization problem for reconstruction that exploits the structure of the spectral X-ray data cube.
A new code aperture design framework for multiframe code aperture snapshot spectral imaging (CASSI) system is presented. It aims at the optimization of code aperture sets such that a group of ...compressive spectral measurements is constructed, each with information from a specific subset of bands. A matrix representation of CASSI is introduced that permits the optimization of spectrally selective code aperture sets. Furthermore, each code aperture set forms a matrix such that rank minimization is used to reduce the number of CASSI shots needed. Conditions for the code apertures are identified such that a restricted isometry property in the CASSI compressive measurements is satisfied with higher probability. Simulations show higher quality of spectral image reconstruction than that attained by systems using Hadamard or random code aperture sets.
Liquid crystal tunable filters (LCTF) are extensively used in hyperspectral imaging systems to successively acquire different spectral components of scenes by adjusting the center wavelength of the ...filter. However, the spectral and spatial resolutions of the imager are limited by the bandwidth of LCTF, and the pitch dimension of the detector, respectively. This paper applies compressive sensing principles to improve both of the spatial and spectral resolutions of the LCTF-based hyperspectral imaging system. An accurate transmission model of the LCTF is used to represent its bandpass filtering effects on the spectra. In addition, a random coded aperture placed behind the LCTF is used to modulate the spectral images in the spatial domain. Then, the three-dimensional encoded spectral images are projected onto a two-dimensional detector. Benefiting from the spectral-dependent transmission property of the LCTF, information of the entire spectrum is collected by a few snapshots using different center wavelengths of the LCTF. Super-resolution hyperspectral images can be reconstructed from a small set of compressive measurements by solving a convex optimization problem. Simulations and experiments show that the proposed method can effectively improve the spectral and spatial resolutions of traditional LCTF-based spectral imager without changing the structures of the LCTF and detector. Finally, a multi-channel spectral coding method is proposed to further increase the compression capacity of the system.
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