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Chinese Journal of Quantum Electronics

Sampling number of image reconstruction arithmetic based on quantum correlated imaging

SU Fen

Published Jan 1, 2015• 0 citations
OpenAlex

Abstract

Quantum correlated imaging technology adopts single-point intensity detecting,huge information storage,slow imaging speed,so faster image reconstruction algorithm was required.The simulation of samples with image reconstructing algorithm was based on statistical arithmetic and compressed sensing respectively.The inputs of the compressed sensing algorithm are sparse images which are calculated with discrete cosine transform(DCT) and Gauss random matrices,reconstructing image with orthogonal matching pursuit(OMP).The result shows that the correlated imaging algorithm based on compressed sensing can lessen the number of measurements and save data space and speed.Therefore,the study of quantum correlated imaging image reconstruction algorithm has great significance for lessening the number of samples and improving imaging speed.

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Quantum correlated imaging technology adopts single-point intensity detecting,huge information storage,slow imaging speed,so faster image reconstruction algorithm was required.The simulation of samples with image reconstructing algorithm was based on statistical arithmetic and compressed sensing respectively.The inputs of the compressed sensing algorithm are sparse images which are calculated with discrete cosine transform(DCT) and Gauss random matrices,reconstructing image with orthogonal matching pursuit(OMP).The result shows that the correlated imaging algorithm based on compressed sensing can lessen the number of measurements and save data space and speed.Therefore,the study of quantum correlated imaging image reconstruction algorithm has great significance for lessening the number of samples and improving imaging speed.
Correlated imaging offers great potentiality, with respect to standard imaging, to obtain the imaging of objects located in optically harsh or noisy environment. It can solve the problems which are difficult to solve by conventional imaging techniques. Recently, it has become one of the hot topics in quantum optics. In this paper, we propose a new scheme of correlated imaging with differential correlated imaging based on compressive sensing, named differential compressive correlated imaging. The new scheme takes advantage of the high signal-to-noise ratio of the differential correlated imaging and low-imaging sampling frequency of the compressed sensing technique. In the scheme, we utilize the intensity of the thermal light, which is in line with the Gaussian distribution, as the measurement matrix of compressive sensing. We extract the differential object information as the image object information which could be recovered via orthogonal matching pursuit algorithm with high quality. By numerical simulations, we verify the proposed scheme. Here, we select the two gray-scale images, such as double-slit and NUPT, as well as the two multi-grayscale images (Lena and Boats) as the object. We take sampling 350 times in differential compressive correlated imaging for measurement. The numerical simulation results show that for the above image objects, the average mean-square error (MSE) over 10 times for the differential compressive correlated imaging scheme is reduced by 97.7%, 93.9%, 92.5% and 71.4% respectively with respect to that of the differential correlated imaging scheme. Moreover, compared with the compressive ghost imaging, the MSE value of the same double-slit in CDGI, as well as Lena and Boats under the same conditions, is reduced by 50.4%, 72.9% and 66.8% separately, which indicates that the compressive differential correlated imaging scheme can greatly improve the signal-to-noise ratio of the imaging, and significantly reduce the imaging time.
In the process of quantum correlation imaging, the use of a fixed sampling mode will affect the imaging efficiency and image reconstruction quality of quantum correlation imaging, and different sampling matrices will have different effects on the imaging results. Aiming at the above problems, this paper proposes an adaptive compressed imaging method based on quantum correlation (ACI). The Digtial Micromirror Devices (DMD) specific sampling mode is designed by distinguishing the different texture details of the image, and the reference light is compressed and sampled by different sampling matrices. Finally, the reference light and the signal light are matched and counted and brought into the reconstruction algorithm to obtain the target image. The experimental results show that the method effectively shortens the imaging time and improves the imaging quality. At the same time, the Toeplitz matrix in the sampling matrix has the best effect. Therefore, this method can make good use of image texture features to improve the quality of quantum correlation imaging while reducing imaging time.
A cost-efficient entangled light quantum imaging method is proposed to improve imaging speed while preserve imaging quality. First of all, the target image is sparsely processed by wavelet transform, and the random Bernoulli matrix is selected as the measurement matrix to ensure the retention of the effective information in the sparse signal. Then, the image is reconstructed by using the Total Variation Augmented Lagrangian Alternating Direction Algorithm (TVAL3) to improve reconstruction accuracy and computation efficiency. Finally, the reconstruction quality of images at different sampling rates is compared and analyzed, and the effectiveness of the proposed method is verified based on an actual optical path of entangled light quantum imaging.
We apply a quantum compressive sensing tomographic reconstruction algorithm to synchrotron radiation data to reconstruct CT images. The sinograms obtained by scanning the artificial molar with a synchrotron are preprocessed and correlated with quantum superposed CT images. To implement our experiments, we compute a QUBO model with10,000\mathbf{1 0, 0 0 0}10,000variables using the hybrid solver of the DWave system. We evaluate the performance of CT images reconstructed by quantum algorithms while reducing the number of projection images used for CT image reconstruction. Quantum algorithms demonstrate potential for reducing total radiation dose in CT image reconstruction.
Quantum imaging (QI) has some unique advantages, such as nonlocal imaging and enhanced space resolution. However, the quality of the reconstructed images and the time of data acquisition leave much to be desired. Based on the framework of compressive sensing, we propose an optimization criterion for high-quality QI whereby total variation restriction is specifically utilized for noise suppression. The corresponding reported algorithm uses a combination of a greedy strategy and the interactive reweight least-squares method. The simulation and the actual imaging experiment both show a significant improvement of the proposed algorithm the over previous imaging method.
Computed tomography (CT) is a non-destructive technique for observing internal images and has proven highly valuable in medical diagnostics. Recent advances in quantum computing have begun to influence tomographic reconstruction techniques. The quantum tomographic reconstruction algorithm is less affected by artifacts or noise than classical algorithms by using the square function of the difference between pixels obtained by projecting CT images in quantum superposition states and pixels obtained from experimental data. In particular, by using quantum linear systems, a fast quadratic unconstrained binary optimization (QUBO) model formulation for quantum tomographic reconstruction is possible. In this paper, we formulate the QUBO model for quantum compressed sensing tomographic reconstruction, which is a linear combination of the QUBO model for quantum tomographic reconstruction and the QUBO model for total variation in quantum superposition-state CT images. In our experiments, we used sinograms obtained by using the Radon transform of Shepp-Logan images and body CT images. We evaluate the performance of the new algorithm by reconstructing CT images using a hybrid solver with the QUBO model computed from each sinogram. The new algorithm was able to obtain a solution within 5 projection images for 30 × 30 image samples and within 6 projection images for 60 × 60 image samples, reconstructing error-free CT images. We anticipate that quantum compressed sensing tomographic reconstruction algorithms could significantly reduce the total radiation dose when quantum computing performance advances.