Comprehensive comparison of single-pixel imaging methods

Lu, Tianao; Qiu, Ziheng; Zhang, Zibang; Zhong, Jingang

doi:10.1016/j.optlaseng.2020.106301

Cited by 58 publications

(17 citation statements)

References 45 publications

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“…With the predefined modulation patterns and correlated detection signals, the object can be reconstructed through matrix inversion. Detailed theory can be found in related reviews [ 31 , 108 , 109 , 110 , 111 ]. Since the intensity and phase patterns are both reconstructable [ 112 ], the imaging system is applicable to various materials, such as metallic resolution chart [ 71 ], knifes [ 65 ], plastic [ 14 ], semitransparent materials [ 113 ], leaves [ 67 ] and circuit boards [ 70 ].…”

Section: Applications and Challengesmentioning

confidence: 99%

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

2022

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Terahertz (THz) technology offers unparalleled opportunities in a wide variety of applications, ranging from imaging and spectroscopy to communications and quality control, where lack of efficient modulation devices poses a major bottleneck. Spatial modulation allows for dynamically encoding various spatial information into the THz wavefront by electrical or optical control. It plays a key role in single-pixel imaging, beam scanning and wavefront shaping. Although mature techniques from the microwave and optical band are not readily applicable when scaled to the THz band, the rise of metasurfaces and the advance of new materials do inspire new possibilities. In this review, we summarize the recent progress of THz spatial light modulators from the perspective of functional materials and analyze their modulation principles, specifications, applications and possible challenges. We envision new advances of this technique in the near future to promote THz applications in different fields.

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Section: Applications and Challengesmentioning

confidence: 99%

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

2022

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“…This imposes a practical limit on applications requiring high resolution images. 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 .…”

Section: Introductionmentioning

confidence: 99%

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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“…Furthermore a limitation that ghost imaging faces is the unsatisfactory imaging speeds. 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 . With each new method, the imaging speed remained unsatisfactory and so too did the number of measurements required to image the object 21 , 22 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning early stopping for non-degenerate ghost imaging

Moodley

Sephton

Rodríguez-Fajardo

et al. 2021

Sci Rep

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Quantum ghost imaging offers many advantages over classical imaging, including the ability to probe an object with one wavelength and record the image with another (non-degenerate ghost imaging), but suffers from slow image reconstruction due to sparsity and probabilistic arrival positions of photons. Here, we propose a two-step deep learning approach to establish an optimal early stopping point based on object recognition, even for sparsely filled images. In step one we enhance the reconstructed image after every measurement by a deep convolutional auto-encoder, followed by step two in which a classifier is used to recognise the image. We test this approach on a non-degenerate ghost imaging setup while varying physical parameters such as the mask type and resolution. We achieved a fivefold decrease in image acquisition time at a recognition confidence of $$75\%$$ 75 % . The significant reduction in experimental running time is an important step towards real-time ghost imaging, as well as object recognition with few photons, e.g., in the detection of light sensitive structures.

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Comprehensive comparison of single-pixel imaging methods

Cited by 58 publications

References 45 publications

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

Recent Progress of Terahertz Spatial Light Modulators: Materials, Principles and Applications

Super-resolved quantum ghost imaging

Deep learning early stopping for non-degenerate ghost imaging

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