X-ray Fourier-transform Ghost Imaging via Sparsity Constraints

Ye, Hong; Lu, Rengui; Tan, Zhijie; Zhu, Rihong; Han, Shensheng

doi:10.1109/nssmic.2017.8532662

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…However, because of the destructive effect of X-ray irradiation, it is almost impossible to multiply encode the sample in the classical CDI. Fortunately, different from CDI techniques, in Fourier-transform GISC, the diffractive pattern of a sample in the testing path can be non-locally encoded by inserting modulation components in the reference path [89]. Therefore, multiple encoding of the sample can be easily realized and better image quality may be expected.…”

Section: X-ray Fourier-transform Giscmentioning

confidence: 99%

“…Therefore, multiple encoding of the sample can be easily realized and better image quality may be expected. Figure 5 shows the principle of coded Fourier-transform GISC [89]. A thermal light beam is split into two paths.…”

Section: X-ray Fourier-transform Giscmentioning

confidence: 99%

“…(a) is the sample, (b,d) are the masks, (c,e) are the non-locally encoded X-ray Fourier-transform GISC diffraction patterns reconstructed from the intensity correlations with (b,d) inserted in the reference path, respectively. (f) is the corresponding coded diffraction pattern obtained from the coherent diffraction imaging [89]. Figure 7 is a photograph of our tabletop X-ray GISC microscope facility.…”

Section: X-ray Fourier-transform Giscmentioning

confidence: 99%

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A Review of Ghost Imaging via Sparsity Constraints

Han

Shen

et al. 2018

Applied Sciences

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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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Section: X-ray Fourier-transform Giscmentioning

confidence: 99%

Section: X-ray Fourier-transform Giscmentioning

confidence: 99%

Section: X-ray Fourier-transform Giscmentioning

confidence: 99%

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A Review of Ghost Imaging via Sparsity Constraints

Han

Shen

et al. 2018

Applied Sciences

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“…It simplifies the light path and improves the performance of ghost imaging. In comparison to traditional imaging, CGI has already prohibited great potentials in applications, such as single-pixel imaging [5,6], biological imaging [7][8][9][10], information encryption [11][12][13], laser radar technique [14,15] and imaging through atmospheric turbulence environment [16][17][18][19][20][21]. However, the reflectivity or transmittance of targets are sometimes close to the background in many real-world scenarios, making it difficult to identify the targets from the background by the received light intensity.…”

Section: Introductionmentioning

confidence: 99%

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

Fan

et al. 2023

Phys. Scr.

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Polarization computational ghost imaging (PCGI) often requires a large number of samples to reconstruct the targets, which can be optimized by reducing sampling rates with the aids of deep-learning technology. In this paper, the random patterns and successive orthonormalization instead of common Hadamard patterns, has been introduced into the deep-learning based PCGI system to recover high-quality images at lower sampling rates. Firstly, we use a polarized light to illuminate the target with random patterns for sampling. Then we can obtain a vector of bucket detector values containing the reflective information of the target. Secondly, we orthonormalize the vector according to the random patterns. Subsequently, the orthonormalized data can be input into the Improved U-net (IU-net) for reconstructing the targets. We demonstrate that higher-quality image of the testing sample can be obtained at a lower sampling rate of 1.5%, and superior-generalization ability for the untrained complex targets can be also achieved at a lower sampling rate of 6%. Meanwhile, we have also investigated the generalization ability of the system for the untrained targets with different materials that have different depolarization properties, and the system still demonstrates superior performances. The proposed method may pave a way towards the real applications of the PCGI.

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X-ray Fourier-transform Ghost Imaging via Sparsity Constraints

Cited by 2 publications

References 13 publications

A Review of Ghost Imaging via Sparsity Constraints

A Review of Ghost Imaging via Sparsity Constraints

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

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