Computed tomography (CT) sequentially interrogates the object of interest from a complete set of view angles. Sequential scanning in CT introduces an acquisition delay and high radiation dose. This paper proposes a compressive sensing based “snapshot” coded X-ray CT (CXCT) method, where the object is simultaneously illuminated by multiple fan-beam X-ray sources equipped with coding masks in a fixed circular gantry. Low radiation dose is achieved by the use of incomplete projection measurements and encoded structured illuminations. Since all the measurement data are produced in one snapshot, the inspection time and motion artifacts are effectively reduced. Due to the overlap of X-rays in the measurements from several sources, a nonlinear reconstruction framework is established based on rank, intensity and sparsity priors. Then, a Newton split Bregman algorithm is exploited to reconstruct the object from a small set of nonlinear encoded measurements. Compared to the state-of-the-art CXCT approaches based on a linear model, the proposed method reduces the inspection time and motion artifacts significantly, achieving higher or comparable reconstruction accuracy.
KEYWORDS: Upconversion, Cameras, Temperature metrology, Infrared imaging, Time metrology, Short wave infrared radiation, Nonlinear crystals, Image quality, Infrared radiation, Signal to noise ratio
In this paper, a low noise infrared imaging based on frequency upconversion is proposed. A function based on the quasi phase-match of two acquired images within 3dB width is formulated to evaluate the maximum step size of the nonlinear crystal temperature for each acquisition. The measurement time is reduced by acquiring images at these specific temperatures. The integration time of each frame is reassigned to improve the signal noise ratio of the acquired images. Comparing scanning the object, our method reduce the noise in background area to 17.14%.
The tight focused pump beam nonlinear frequency upconversion based on Hadamard coding is presented to acquire converted photon images. The pump beam is optimized by tight focusing to enhance its power
density in nonlinear crystal. In order to reduce the distortion caused by the point spread function effect, the object is encoded by measurement matrices and the converted photons corresponding to each pattern is measured. Thus the converted image with sharp edges can be reconstructed by the measurements and the measurement matrices. In the experiment, the image with 64 × 64 pixels is acquired and the peak of the dark noise is less than0.7photons/spatial/second element for 10 ms measurement time.
Photon-limited coded aperture compressive temporal imaging (PL-CACTI) is proposed and analyzed in this manuscript to alleviate the tradeoff between the exposure time and frame rate. Multiple high-speed frames modulated by coded apertures in low-light-level (LLL) environments are down-sampled by a high-sensitivity camera with low frame rates. Compressive sensing (CS) is used to reconstructed the high-speed images from the multiplexed measurements. In this manuscript, four data sets with 20fps videos are used at different compression ratios (Cr) to demonstrate the performance of PL-CACTI. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the images reconstructed from a set of blurred coded projections are analyzed. A mixture model for LLL measurements with Poisson noise and Gaussian noise is simulated with a 25fps LLL camera where the data sets are captured by a high-speed camera at 100fps and 200fps models, respectively. Results of simulated data sets and real data sets show that CACTI performs well in the photon limited conditions where the PSNRs are more than 25dB and SSIMs are more than 0.8. Therefore, PL-CACTI is an alternative approach to realize high-speed imaging in LLL environments subject.
In this paper, a coded aperture optimization approach based on sparse principal component analysis (SPCA) is proposed to maximize the information sensed by a set of cone-beam projections. The variables in the CT system matrix correspond to observations of the attenuation characteristics of X-ray projections. An adjusted joint variance is used to update the variables and thus the overlapping information of the kth principal component is constrained by the previous k-1 principal components. Since the coded aperture matrix is diagonal and binary, an efficient algorithm is proposed to reduce the complexity by one order of magnitude. Simulations using simulated datasets, 3D Shepp-Logan phantom, show significant gains up to 23.5dB compared with that attained by random coded apertures. Singular value decomposition (SVD) of the optimized coded apertures is used to analyze the performance of the proposed coded aperture optimization method based on SPCA.
The CT system structure matrix in the coded aperture compressive X-ray tomography (CACXT) is highly structured and thus the random coded apertures are not optimal. A fast approach based on minimal information loss is proposed. The peak signal to noise ratios (PSNR) of the reconstructed images with optimized coded apertures exhibit significant gains and the design execution time is reduced by orders of magnitude. Simulations results for optimized coded apertures are shown, and their performance is compared to the use of random coded apertures.
Coded aperture snapshot spectral imager (CASSI) uses focal plane array (FPA) to capture three dimensional (3D) spectral scene by single or a few two-dimensional (2D) snapshots. Current CASSI systems use a set of fixed coded apertures to modulate the spatio-spectral data cube before the compressive measurement. This paper proposes an adaptive projection method to improve the compressive efficiency of the CASSI system by adaptively designing the coded aperture according to a-priori knowledge of the scene. The adaptive coded apertures are constructed from the nonlinear thresholding of the grey-scale map of the scene, which is captured by an aided RGB camera. Then, the 3D encoded spectral scene is projected onto the 2D FPAs. Based on the sparsity assumption, the spectral images can be reconstructed by the compressive sensing algorithm using the FPA measurements. This paper studies and verifies the proposed adaptive coded aperture method on a spatial super-resolution CASSI system, where the resolution of the coded aperture is higher than that of the FPAs. It is shown that the adaptive coded apertures provide superior reconstruction performance of the spectral images over the random coded apertures.
A photon counting 3D imaging system with short-pulsed structured illumination and a
single-pixel photon counting detector is built. The proposed multiresolution photon counting 3D
imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully
finer resolution sampled by Hadamard multiplexing along with the wavelet trees. The detected power is
significant increased thanks to the Hadamard multiplexing. Both the required measurements and the
reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes
with high spatial resolution. Since the depth map is retrieved through a linear inverse Hadamard
transform instead of the computational intensive optimization problems performed in CS, the time
consumed to retrieve the depth map can be also reduced, and thus it will be suitable for applications of
real-time compressed 3D imaging such as object tracking. Even though the resolution of the final 3D
image can be high, the number of measurements remains small due to the adaptivity of the
wavelet-trees-based sampling strategy. The adaptive sampling technique is quality oriented, allowing
more control over the image quality. The experimental results indicate that both the intensity image and
depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical
times as low as 17 seconds.
Incoherent Coincidence Imaging (ICI), which is based on the second or higher order correlation of fluctuating light field, has provided great potentialities with respect to standard conventional imaging. However, the deployment of reference arm limits its practical applications in the detection of space objects. In this article, an optical aperture synthesis with electronically connected single-pixel photo-detectors was proposed to remove the reference arm. The correlation in our proposed method is the second order correlation between the intensity fluctuations observed by any two detectors. With appropriate locations of single-pixel detectors, this second order correlation is simplified to absolute-square Fourier transform of source and the unknown object. We demonstrate the image recovery with the Gerchberg-Saxton-like algorithms and investigate the reconstruction quality of our approach. Numerical experiments has been made to show that both binary and gray-scale objects can be recovered. This proposed method provides an effective approach to promote detection of space objects and perhaps even the exo-planets.
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