Imaging reconstruction comparison of different ghost imaging algorithms

Liu, Hong‐Chao

doi:10.1038/s41598-020-71642-2

Cited by 15 publications

(10 citation statements)

References 43 publications

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“…Traditionally the image is reconstructed as a linear combination of all masks weighted by the coincidences and is expressed as the traditional second order ghost imaging (TGI) reconstruction algorithm 21 : where I is the image, the coincidence counts, and the mask for the measurement. We refer to as the coincidence counts for ease of understanding as we weight the masks by the respective coincidence counts to recontruct the image, however is the overlap between the object and mask - which is proportional to the coincidence counts.…”

Section: Image Reconstruction Methodsmentioning

confidence: 99%

“…Earlier raster scanning implementations were used 5 , which evolved to more timely methods using a single-pixel bucket detector and pre-computed binary intensity fluctuation patterns 17 , single-pixel scanning methods 18 , 19 and Fourier single-pixel scanning methods 20 . The imaging speed remained unsatisfactory and so too did the number of measurements required to reconstruct the image 21 , 22 . Attempts to improve and enhance image quality and resolution focused on employing a pseudo-inverse ghost imaging technique via a sparsity constraint 23 , employing a Schmidt decomposition for image enhancement 24 , and imaging based on Fourier spectrum acquisition 25 .…”

Section: Introductionmentioning

confidence: 99%

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Super-resolved quantum ghost imaging

Moodley

Forbes

2022

Sci Rep

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Quantum ghost imaging offers many advantages over classical imaging, including low photon fluxes and non-degenerate object and image wavelengths for imaging light sensitive structures, but suffers from slow image reconstruction speeds. Image reconstruction times depend on the resolution of the required image which scale quadratically with the image resolution. Here, we propose a super-resolved imaging approach based on neural networks where we reconstruct a low resolution image, which we denoise and super-resolve to a high resolution image. To test the approach, we implemented both a generative adversarial network as well as a super-resolving autoencoder in conjunction with an experimental quantum ghost imaging setup, demonstrating its efficacy across a range of object and imaging projective mask types. We achieved super-resolving enhancement of $$4\times$$ 4 × the measured resolution with a fidelity close to 90$$\%$$ % at an acquisition time of N$$^2$$ 2 measurements, required for a complete N $$\times$$ × N pixel image solution. This significant resolution enhancement is a step closer to a common ghost imaging goal, to reconstruct images with the highest resolution and the shortest possible acquisition time.

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Section: Image Reconstruction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Super-resolved quantum ghost imaging

Moodley

Forbes

2022

Sci Rep

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“…In practice, CS reconstruction algorithms solve a convex optimization problem, looking for the image which minimizes the L 1 −norm in the sparse basis among the ones compatible with the bucket measurements, see Refs. [49][50][51][52] for a review.…”

Section: Compressive Sensingmentioning

confidence: 99%

Towards Quantum 3D Imaging Devices

et al. 2021

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We review the advancement of the research toward the design and implementation of quantum plenoptic cameras, radically novel 3D imaging devices that exploit both momentum–position entanglement and photon–number correlations to provide the typical refocusing and ultra-fast, scanning-free, 3D imaging capability of plenoptic devices, along with dramatically enhanced performances, unattainable in standard plenoptic cameras: diffraction-limited resolution, large depth of focus, and ultra-low noise. To further increase the volumetric resolution beyond the Rayleigh diffraction limit, and achieve the quantum limit, we are also developing dedicated protocols based on quantum Fisher information. However, for the quantum advantages of the proposed devices to be effective and appealing to end-users, two main challenges need to be tackled. First, due to the large number of frames required for correlation measurements to provide an acceptable signal-to-noise ratio, quantum plenoptic imaging (QPI) would require, if implemented with commercially available high-resolution cameras, acquisition times ranging from tens of seconds to a few minutes. Second, the elaboration of this large amount of data, in order to retrieve 3D images or refocusing 2D images, requires high-performance and time-consuming computation. To address these challenges, we are developing high-resolution single-photon avalanche photodiode (SPAD) arrays and high-performance low-level programming of ultra-fast electronics, combined with compressive sensing and quantum tomography algorithms, with the aim to reduce both the acquisition and the elaboration time by two orders of magnitude. Routes toward exploitation of the QPI devices will also be discussed.

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“…In practice, CS reconstruction algorithms solve a convex optimization problem, seeking for the image which minimizes the L 1 −norm in the sparse basis among the ones compatible with the bucket measurements, see Refs. [38][39][40][41] for a review.…”

Section: A Compressive Sensingmentioning

confidence: 99%

Towards quantum 3D imaging devices: the Qu3D project

Abbattista¹,

Amoruso²,

Burri³

et al. 2021

Preprint

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We review the advancement of the research toward the design and implementation of quantum plenoptic cameras, radically novel 3D imaging devices that exploit both momentumposition entanglement and photon-number correlations to provide the typical refocusing and ultra-fast, scanning-free, 3D imaging capability of plenoptic devices, along with dramatically enhanced performances, unattainable in standard plenoptic cameras: diffraction-limited resolution, large depth of focus, and ultra-low noise. To further increase the volumetric resolution beyond the Rayleigh diffraction limit, and achieve the quantum limit, we are also developing dedicated protocols based on quantum Fisher information. However, for the quantum advantages of the proposed devices to be effective and appealing to end-users, two main challenges need to be tackled. First, due to the large number of frames required for correlation measurements to provide an acceptable SNR, quantum plenoptic imaging would require, if implemented with commercially available high-resolution cameras, acquisition times ranging from tens of seconds to a few minutes. Second, the elaboration of this large amount of data, in order to retrieve 3D images or refocusing 2D images, requires highperformance and time-consuming computation. To address these challenges, we are developing high-resolution SPAD arrays and high-performance low-level programming of ultra-fast electronics, combined with compressive sensing and quantum tomography algorithms, with the aim to reduce both the acquisition and the elaboration time by two orders of magnitude. Routes toward exploitation of the QPI devices will also be discussed.

show abstract

Imaging reconstruction comparison of different ghost imaging algorithms

Cited by 15 publications

References 43 publications

Super-resolved quantum ghost imaging

Super-resolved quantum ghost imaging

Towards Quantum 3D Imaging Devices

Towards quantum 3D imaging devices: the Qu3D project

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