Deep-learning-based ghost imaging

Lyu, Meng; Wang, Wei; Wang, Hao; Wang, Haichao; Li, Guo-Wei; Ni, Chen; Situ, Guohai

doi:10.1038/s41598-017-18171-7

Cited by 296 publications

(138 citation statements)

References 29 publications

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“…Deep learning has been adopted in single-pixel imaging for reducing data acquisition time or improving image reconstruction quality [29][30][31][32]. Our method adopts deep learning to achieve adaptive spatial light modulation patterns generation and objects classification using single-pixel measurements, which might generate a new insight for spatial light modulation patterns optimization by using deep learning.…”

Section: Discussionmentioning

confidence: 99%

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Zhang¹,

Li²,

Zheng³

et al. 2020

Opt. Express

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Objects classification generally relies on image acquisition and analysis. Real-time classification of high-speed moving objects is challenging, as both high temporal resolution in image acquisition and low computational complexity in objects classification algorithms are required. Here we propose and experimentally demonstrate an approach for real-time moving objects classification without image acquisition. As objects classification algorithms rely on the feature information of objects, we propose to use spatial light modulation to acquire the feature information directly rather than performing image acquisition followed by features extraction. A convolutional neural network is designed and trained to learn the spatial features of the target objects. The trained network can generate structured patterns for spatial light modulation. Using the resulting structured patterns for spatial light modulation, the feature information of target objects can be compressively encoded into a short light intensity sequence. The resulting one-dimensional signal is collected by a single-pixel detector and fed to the convolutional neural network for objects classification. As experimentally demonstrated, the proposed approach can achieve accurate and real-time classification of fast moving objects. The proposed method has potential applications in the fields where fast moving objects classification in real time and for long duration is required.

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Section: Discussionmentioning

confidence: 99%

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Zhang¹,

Li²,

Zheng³

et al. 2020

Opt. Express

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“…and control [24][25][26][27][28][29]. In addition, several studies for phase retrieval, ghost imaging, and superresolution imaging with deep learning have been reported [30][31][32]. In this paper, we modify a deep convolutional residual network (ResNet [33]) for each of the inversions.…”

Section: 2 Methodsmentioning

confidence: 99%

Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning

Horisaki

Fujii

Tanida

2018

Opt Rev

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We present a single-shot incoherent light imaging method for simultaneously observing both amplitude and phase without any imaging optics, based on machine learning. In the proposed method, an object with a complex-amplitude field is illuminated with incoherent light and is captured by an image sensor with or without a coded aperture. The complex-amplitude field of the object is reconstructed from a single captured image using a state-of-the-art deep convolutional neural network, which is trained with a large number of input and output pairs. In experimental demonstrations, the proposed method was verified with a handwritten character database, and the effect of a coded aperture printed on an overhead projector film in the reconstruction was examined. Our method has advantages over conventional wavefront sensing techniques using incoherent light, namely simplification of the optical hardware and improved measurement speed. This study shows the importance and practical impact of machine learning techniques in various fields of optical sensing.

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“…But the training is usually time-consuming and the resulting network architecture is hard to generalize as aforementioned. 25,33 It typically takes many hours or even days to train depending on the size of the training set and the geometry of the network. For example, the fully connected DNN model that we developed for imaging through scattering 25 took us 18 h to train with a Tesla K20c GPU.…”

Section: Hnn Modelmentioning

confidence: 99%

“…Second, even though the training can converge eventually, overfitting is very likely to occur. 25,33 Third, the trained network is hard to generalize. In this paper, we demonstrate that the above drawbacks can be overcome using an HNN model.…”

Section: Introductionmentioning

confidence: 99%

Learning-based lensless imaging through optically thick scattering media

et al. 2019

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The problem of imaging through thick scattering media is encountered in many disciplines of science, ranging from mesoscopic physics to astronomy. Photons become diffusive after propagating through a scattering medium with an optical thickness of over 10 times the scattering mean free path. As a result, no image but only noise-like patterns can be directly formed. We propose a hybrid neural network for computational imaging through such thick scattering media, demonstrating the reconstruction of image information from various targets hidden behind a white polystyrene slab of 3 mm in thickness or 13.4 times the scattering mean free path. We also demonstrate that the target image can be retrieved with acceptable quality from a very small fraction of its scattered pattern, suggesting that the speckle pattern produced in this way is highly redundant. This leads to a profound question of how the information of the target being encoded into the speckle is to be addressed in future studies.

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Deep-learning-based ghost imaging

Cited by 296 publications

References 29 publications

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Image-free classification of fast-moving objects using “learned” structured illumination and single-pixel detection

Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning

Learning-based lensless imaging through optically thick scattering media

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