Coherent Ghost Imaging based on sparsity constraint without phase-sensitive detection

Different from conventional imaging methods, which are based on the first-order field correlation, ghost imaging (GI) obtains the image information through high-order mutual-correlation of light fields from two paths with an object appearing in only one path. As a new optical imaging technology, GI not only provides us new capabilities beyond the conventional imaging methods, but also gives out a new viewpoint of imaging physical mechanism. It may be applied to many potential applications, such as remote sensing, snap-shot spectral imaging, thermal X-ray diffraction imaging and imaging through scattering media. In this paper, we reviewed mainly our research work of ghost imaging via sparsity constraints (GISC) and discussed the application and theory prospect of GISC concisely.

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“…Equation 3is the regularized framework of GISC. At present, the data fidelity term in Equation (3) is usually approximated as [26][27][28] min…”

Section: Theoretical Framework Of Giscmentioning

confidence: 99%

A Review of Ghost Imaging via Sparsity Constraints

Han

Shen

et al. 2018

Applied Sciences

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“…Unlike the conventional direct point-to-point imaging mode, the resolution of the pixels of ghost imaging is determined by the correlation of light field fluctuations corresponding to the two pixels respectively, which can be measured on-line or pre-determined. 11,12 Combining with compressive sensing (CS) theory 1,[13][14][15][16][17] , ghost imaging via sparsity constraints (GISC) has many potential applications including super-resolution imaging [18][19][20][21] , three-dimensional (3D) computational imaging with single-pixel detectors 22 , 3D remote sensing 23,24 , imaging through scattering media 25,26 , object tracking 27 , object authentication 28,29 and X-ray Fourier transform diffraction imaging [30][31][32] . For thermal light ghost imaging, according to the illumination source, it can be classified to two categories: ghost imaging with pseudo-thermal light and true thermal light.…”

Section: Introductionmentioning

confidence: 99%

Spectral Camera based on Ghost Imaging via Sparsity Constraints

Liu

Tan

et al. 2016

Sci Rep

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The image information acquisition ability of a conventional camera is usually much lower than the Shannon Limit since it does not make use of the correlation between pixels of image data. Applying a random phase modulator to code the spectral images and combining with compressive sensing (CS) theory, a spectral camera based on true thermal light ghost imaging via sparsity constraints (GISC spectral camera) is proposed and demonstrated experimentally. GISC spectral camera can acquire the information at a rate significantly below the Nyquist rate, and the resolution of the cells in the three-dimensional (3D) spectral images data-cube can be achieved with a two-dimensional (2D) detector in a single exposure. For the first time, GISC spectral camera opens the way of approaching the Shannon Limit determined by Information Theory in optical imaging instruments.

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“…Ghost imaging has attracted much attention these years for its ability to acquire signals from sampling beyond Nyquist limit when combined with compressive sensing [1][2][3][4] and its potential applications in remote sensing, super-resolution microscopy, morphology component analysis, diffraction imaging, etc [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In order to accurately determine the unknown targets in remote sensing, a large number of measurements are required in ghost imaging lidar, which limits its practical application significantly.…”

Section: Introductionmentioning

confidence: 99%

Structured image reconstruction for three-dimensional ghost imaging lidar

Ye¹,

Li²,

Gong³

et al. 2015

Opt. Express

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A structured image reconstruction method has been proposed to obtain high quality images in three-dimensional ghost imaging lidar. By considering the spatial structure relationship between recovered images of scene slices at different longitudinal distances, orthogonality constraint has been incorporated to reconstruct the three-dimensional scenes in remote sensing. Numerical simulations have been performed to demonstrate that scene slices with various sparse ratios can be recovered more accurately by applying orthogonality constraint, and the enhancement is significant especially for ghost imaging with less measurements. A simulated three-dimensional city scene has been successfully reconstructed by using structured image reconstruction in three-dimensional ghost imaging lidar.

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Coherent Ghost Imaging based on sparsity constraint without phase-sensitive detection

Cited by 17 publications

References 18 publications

A Review of Ghost Imaging via Sparsity Constraints

A Review of Ghost Imaging via Sparsity Constraints

Spectral Camera based on Ghost Imaging via Sparsity Constraints

Structured image reconstruction for three-dimensional ghost imaging lidar

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