Ghost diffraction holographic microscopy

Vinu, R. V.; Chen, Ziyang; Singh, Rakesh Kumar; Pu, Jixiong

doi:10.1364/optica.409886

Cited by 49 publications

(20 citation statements)

References 58 publications

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“…com/article/10.3390/app112412167/s1. References [28,[31][32][33][34]37] are cited in the supplementary materials.…”

Section: Discussionmentioning

confidence: 99%

“…From Equation (3), it is evident that this intensity cross-correlation retrieves only the modulus of W(r 1 , r 2 ); therefore, for the complex correlation function recovery and thereby the simultaneous recovery of amplitude and phase distributions of the OAM mode, we make use of the holography scheme, where a reference random field is combined with the propagated spatially incoherent field as shown in Figure 1. The independent addition of the holographic reference random field [31,32] (ψ R (r)) with the field contributions ψ m (r) and ψ 0 (r) modifies the correlation of intensity fluctuations in Equation (3) to:…”

Section: Generation and Recovery Of Oam Modesmentioning

confidence: 99%

“…the reference correlation function. Albeit the execution of ensemble averaging in the approach can be performed either with temporal averaging or with space averaging, we rely on space averaging by considering the time frozen speckle fields at the detector plane under the assumption of spatial stationarity [32][33][34][35]. The spatial averaging is performed in the execution of the cross-correlation of intensity images by fixing the distance ∆r = r 1 − r 2 equivalent to the pixel size at the detector plane, and by moving a specific window size in the intensity distribution I m (r) over the entire intensity image I 0 (r).…”

Section: Generation and Recovery Of Oam Modesmentioning

confidence: 99%

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Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography

et al. 2021

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Orbital angular momentum (OAM) of optical vortex beams has been regarded as an independent physical dimension of light with predominant information-carrying potential. However, the presence of scattering environment and turbulent atmosphere scrambles the helical wavefront and destroys the orthogonality of modes in vortex beam propagation. Here, we propose and experimentally demonstrate a new basis for the recovery of the OAM mode using a holographic ghost diffraction scheme. The technique utilizes the speckle field generated from a rotating diffuser for optical vortex mode encoding, and the fourth-order correlation of the speckle field for the efficient recovery of the associated modes. Furthermore, we successfully demonstrate the complex-field recovery of OAM modes by the adoption of a holography scheme in combination with the ghost diffraction system. We evaluate the feasibility of the approach by simulation and followed by experimental demonstration for the recovery of various sequentially encoded OAM modes. Finally, the efficacy of the recovered modes was quantitatively analyzed by an OAM mode analysis utilizing orthogonal projection scheme.

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“…com/article/10.3390/app112412167/s1. References [28,[31][32][33][34]37] are cited in the supplementary materials.…”

Section: Discussionmentioning

confidence: 99%

Section: Generation and Recovery Of Oam Modesmentioning

confidence: 99%

Section: Generation and Recovery Of Oam Modesmentioning

confidence: 99%

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Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography

et al. 2021

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“…The other is single-pixel camera (SPC), where the target is usually imaged onto a spatial modulation device and the modulated signals are received by a single-pixel detector [6,7]. In comparison with SPC, more investigations were focused on GI because of its potential applications in remote sensing [10][11][12], three-dimensional imaging [13][14][15], phase imaging [16][17][18][19], optical encryption [20,21], and microwave imaging [22]. Recently, some works have demonstrated that SPC has more advantages than GI in remote sensing [8,9,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Disturbance-free single-pixel imaging camera based on P=0.5 Bernoulli modulation and complementary detection

Gong¹

2022

Preprint

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We present a technique called single-pixel imaging camera based on complementary detection and optimized Bernoulli modulation (CSPI camera), which can significantly reduce the influence of the disturbance light to single-pixel imaging (SPI). The numerical simulation demonstrates that when the probability of the value "1" is P=0.5 for each coded pattern, CSPI camera is disturbance-free is still disturbance-free even if the intensity fluctuation of the disturbance light is much larger than the signal's intensity. The reconstruction results of both SPI and differential SPI are also compared. This technique of CSPI camera can dramatically promote real application of single-pixel imaging Lidar.

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“…The deep neural networks can solve many remaining problems in fields of scattering imaging, computational ghost imaging, Fourier laminar microscopic imaging, phase retrieval and hologram data compression etc. [5][6][7][8][9][10][11]. It is shown that as an object passes through a scattering medium or multimode fiber (MMF), the information of the image is deteriorated.…”

Section: Introductionmentioning

confidence: 99%

Mutual Transfer Learning of Reconstructing Images Through a Multimode Fiber or a Scattering Medium

Lai

et al. 2021

IEEE Access

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In recent years, convolutional neural network (CNN) has been successfully applied to reconstruct image from the speckle, which is generated as an object passes through a scattering medium or a multimode fiber (MMF). To reconstruct image from the speckle, the CNN must be trained with a large number of object-speckle pairs (training dataset), and the trained CNN is capable of reconstructing image from dataset (test dataset), which is taken in the same condition as the training dataset. However, in some cases, data type and the scattering medium may vary with the situation. In this case, the CNN has to be retrained using a large number of new data taken from the new scattering media for reconstructing image. In this paper, we develop a CNN called as Mobiledense-net (MDN) to realize the mutual transfer learning. Specifically, the MDN is first pre-trained with a large number of object-speckle pairs taken from MMF or scattering slab, then tuned with quite small number of object-speckle pairs from scattering slab or MMF. It is shown that in this case the MDN can reconstruct image from the speckle with quite good quality, in which the speckle is taken from MMF or the scattering slab. We also show that using a more complex dataset for pre-training, the amount of data for pre-training can be largely reduced and reconstruction quality can be further improved. Using transfer learning, the reconstruction quality is quite good, being up to 99% . The results in this paper provide a more generalized method for studying the imaging through scattering imaging or MMF by using CNN.INDEX TERMS Imaging, convolutional neural network (CNN), Mobiledense-net (MDN), transfer learning, scattering medium, multimode fiber (MMF).

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Ghost diffraction holographic microscopy

Cited by 49 publications

References 58 publications

Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography

Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography

Disturbance-free single-pixel imaging camera based on P=0.5 Bernoulli modulation and complementary detection

Mutual Transfer Learning of Reconstructing Images Through a Multimode Fiber or a Scattering Medium

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