Differential Ghost Imaging

Ferri, F.; Magatti, D.; Lugiato, L. A.; Gatti, A.

doi:10.1103/physrevlett.104.253603

Cited by 644 publications

(337 citation statements)

References 22 publications

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“…(4) are both identical however the first term in brackets of Eq. (4) is now weighted according to the average value of S, which is normalized to the average value of R. As demonstrated in [9] the DGI algorithm improves by order of magnitude the SNR of the measurement with respect to TGI. Moreover, a key difference from TGI, it is no longer sensitive to other sources of noise.…”

Section: Differential Ghost Imagingmentioning

confidence: 99%

“…Differential GI [9], first performed by Ferri et al, utilizes a second bucket detector to extract a reference signal which is used in the reconstruction to weight the speckle field based on the average transmission signal relative to the average reference signal. Similarly, each contribution to the reconstruction can be expressed as…”

Section: Differential Ghost Imagingmentioning

confidence: 99%

“…In this paper we compare a number of these iterative algorithms within the context of computational GI. The algorithms we consider are traditional GI (TGI) and differential GI (DGI) [9]. In a computational GI setup, TGI uses a weighting factor equal to the signal from the detector whereas DGI utilizes a weighting factor that depends on fluctuations in the measured signal and uses an additional detector to give a normalization.…”

Section: Introductionmentioning

confidence: 99%

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Normalized ghost imaging

Sun¹,

Welsh²,

Edgar³

et al. 2012

Opt. Express

325

172

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Abstract:We present an experimental comparison between different iterative ghost imaging algorithms. Our experimental setup utilizes a spatial light modulator for generating known random light fields to illuminate a partially-transmissive object. We adapt the weighting factor used in the traditional ghost imaging algorithm to account for changes in the efficiency of the generated light field. We show that our normalized weighting algorithm can match the performance of differential ghost imaging.

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Section: Differential Ghost Imagingmentioning

confidence: 99%

Section: Differential Ghost Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Normalized ghost imaging

Sun¹,

Welsh²,

Edgar³

et al. 2012

Opt. Express

325

172

View full text Add to dashboard Cite

show abstract

“…To achieve this, the key point is to increase the SNR in measurement. Several approaches aiming at increasing the SNR have been proposed, such as differential measurement 6,10,11 , illumination pattern normalization 7,10,11 , frequency-locked measurement 10,11 and so on. However, to our knowledge, no single-pixel technique can successfully achieve high-quality imaging with indirect measurement so far.…”

mentioning

confidence: 99%

“…Later single-pixel imaging techniques that use a classical light source to exploit Helmholtz reciprocity 3 have been proposed. These techniques include GI [4][5][6][7] , computational imaging [8][9][10][11] and dual photography 1,12,13 . These single-pixel techniques can potentially capture a scene with indirect measurement where detectors sample the indirect light only.…”

mentioning

confidence: 99%

Single-pixel imaging by means of Fourier spectrum acquisition

2015

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Single-pixel imaging techniques enable to capture a scene without a direct line of sight to the object, but high-quality imaging has been proven challenging especially in the presence of noisy environmental illumination. Here we present a single-pixel imaging technique that can achieve high-quality images by acquiring their Fourier spectrum. We use phase-shifting sinusoid structured illumination for the spectrum acquisition. Applying inverse Fourier transform to the obtained spectrum yields the desired image. The proposed technique is capable of capturing a scene without a direct view of it. Thus, it enables a feasible placement of detectors, only if the detectors can collect the light signals from the scene. The technique is also a compressive sampling like approach, so it can reconstruct an image from sub-Nyquist measurements. We experimentally obtain clear images by utilizing a detector not placed in direct view of the imaged scene even with noise introduced by environmental illuminations.

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Achieving 256 × 256‐Pixel Color Images by Perovskite‐Based Photodetectors Coupled with Algorithms

Liu

Yao

et al. 2021

Adv Funct Materials

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Perovskites have been found with their exceptional optoelectronic properties that may bring new options to high‐performance cameras. However, the best images obtained by perovskite‐based photodetectors (PDs) have only 32 × 32 pixels, far below megapixels of camera images. In this study, a perovskite single‐PD color imaging prototype coupled with computation algorithms is fabricated, by which images with 256 × 256 pixels, whose quality has prevailed over those obtained by any perovskite‐based device so far are obtained. The technology shows its unique advantages by obtaining satisfying photographs, even when the PD is covered with frosted glass/moving smoke or facing a wall instead of the target, in addition to possessing some ability to resist ambient light interference. Moreover, a new Q factor is proposed to evaluate new‐material‐based PD for the imaging technology and prove its usability by comparing an atomic‐layer‐deposition (ALD)‐Al2O3 optimized CsBi3I10 PD and an unoptimized CsBi3I10 PD. Finally, the material choices are successfully extended to other types of perovskites, including photovoltaic MAPbI3 PD (structure: Spiro‐OMeTAD/MAPbI3/SnO2/FTO), ALD‐Al2O3 MAPbI3 PD, and ALD‐Al2O3 CsPbBr3 PD. The technology may bring an innovative approach to improve the performance of cameras and extend the application range of advanced functional materials.

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Differential Ghost Imaging

Cited by 644 publications

References 22 publications

Normalized ghost imaging

Normalized ghost imaging

Single-pixel imaging by means of Fourier spectrum acquisition

Achieving 256 × 256‐Pixel Color Images by Perovskite‐Based Photodetectors Coupled with Algorithms

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