Fourier single-pixel imaging using fewer illumination patterns

Deng, Huaxia; Gao, Xicheng; Ma, Mengchao; Yao, Pengcheng; Guan, Qingtian; Zhong, Xiang; Zhang, Jin

doi:10.1063/1.5097901

Cited by 42 publications

(9 citation statements)

References 29 publications

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“…In terms of sampling, although there are many compressed sensing technologies applied to improve the imaging efficiency of SPI [ 15 , 16 , 34 , 35 , 36 , 37 ], the time-consuming reconstruction process limits their application in real-time imaging [ 27 ]. Conventional FSI has the advantage of simple reconstruction for only one IFT is required, and it has been applied in real-time and high-quality imaging [ 29 , 32 ]. The reported method CFSI combines the advantages of current methods and improves the performance of conventional FSI.…”

Section: Discussionmentioning

confidence: 99%

“…Given the quantization errors and reduction in the imaging spatial resolution caused by spatial dithering strategy [ 32 ], the imaging quality is worse than that of the grayscale FSI. The imaging quality of the two-step phase-shift FSI is worse than that of the three other methods because its base pattern is not applicable after spatial dithering [ 29 ]. Qualitative comparison indicates that as the number of measurements increase, the PSNR values of binary CFSI and three- and four-step phase-shift binary FSI increases first and then decreases.…”

Section: Simulations and Experimentsmentioning

confidence: 99%

“…In 2017, Zhang et al [ 27 ] proposed an FSI based on three-step phase-shift algorithm that only requires 1.5-times the number of pixels of the illumination pattern. In 2019, Deng et al [ 29 ] proposed an FSI based on a two-step phase-shift algorithm and demonstrated that it can perform full sampling measurements equivalent to the number of the pixels of the illumination pattern. However, the three- and two-step phase-shift algorithms sacrifice noise robustness to reduce the number of measurements.…”

Section: Introductionmentioning

confidence: 99%

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Complementary Fourier Single-Pixel Imaging

Zhou

Cao

Hao

et al. 2021

Sensors

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Single-pixel imaging, with the advantages of a wide spectrum, beyond-visual-field imaging, and robustness to light scattering, has attracted increasing attention in recent years. Fourier single-pixel imaging (FSI) can reconstruct sharp images under sub-Nyquist sampling. However, the conventional FSI has difficulty balancing imaging quality and efficiency. To overcome this issue, we proposed a novel approach called complementary Fourier single-pixel imaging (CFSI) to reduce the number of measurements while retaining its robustness. The complementary nature of Fourier patterns based on a four-step phase-shift algorithm is combined with the complementary nature of a digital micromirror device. CFSI only requires two phase-shifted patterns to obtain one Fourier spectral value. Four light intensity values are obtained by loading the two patterns, and the spectral value is calculated through differential measurement, which has good robustness to noise. The proposed method is verified by simulations and experiments compared with FSI based on two-, three-, and four-step phase shift algorithms. CFSI performed better than the other methods under the condition that the best imaging quality of CFSI is not reached. The reported technique provides an alternative approach to realize real-time and high-quality imaging.

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Section: Discussionmentioning

confidence: 99%

Section: Simulations and Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Complementary Fourier Single-Pixel Imaging

Zhou

Cao

Hao

et al. 2021

Sensors

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“…Recent studies on single-pixel imaging show that the theory of linear propagation of light and the theory of keyhole imaging can be broken through, and object reconstruction can be performed based on one-dimensional data [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. To reduce sampling data for single-pixel imaging, a set of modulated patterns is used to realize compressed sampling [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. It improves the speed of single-pixel microscopic imaging.…”

Section: Introductionmentioning

confidence: 99%

Transmissive Single-Pixel Microscopic Imaging through Scattering Media

Deng

Wang

et al. 2021

Sensors

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Microscopic imaging is of great significance for medical diagnosis. However, due to the strong scattering and absorption of tissue, the implementation of non-invasive microscopic imaging is very difficult. Traditional single-pixel microscopes, based on reflective optical systems, provide an alternative solution for scattering media imaging. Here, the single-pixel microscope with transmissive liquid crystal modulation is proposed. The microscopic ability of the proposed microscope is calibrated. The multi-spectral microscopic imaging of the object is demonstrated. The transmissive imaging of the object behind the scattering media is analyzed. The proposed prototype of the transmissive single-pixel microscope is expected to be applied in microscopic imaging through scattering media and medical imaging.

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“…These reconstructions generally involve solving an inverse problem and retrieving the phase from phase-less intensity measurements. The field has been an active area of research for several decades [5][6][7] and inverse solvers achieve impressive results with additional coded optics or optical scanning [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. More recently, deep neural networks, and specifically convolutional neural networks, enable single feed-forward, non-iterative reconstruction [22] and are capable of learning from the statistical information contained in a variety of systems, from speckle [23,24] to coded diffraction [25] patterns.…”

Section: Introductionmentioning

confidence: 99%

Toward simple, generalizable neural networks with universal training for low-SWaP hybrid vision

et al. 2021

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Speed, generalizability, and robustness are fundamental issues for building lightweight computational cameras. Here we demonstrate generalizable image reconstruction with the simplest of hybrid machine vision systems: linear optical preprocessors combined with nohidden-layer, "small-brain" neural networks. Surprisingly, such simple neural networks are capable of learning the image reconstruction from a range of coded diffraction patterns using two masks. We investigate the possibility of generalized or "universal training" with these small brains. Neural networks trained with sinusoidal or random patterns uniformly distribute errors around a reconstructed image, whereas models trained with a combination of sharp and curved shapes (the phase pattern of optical vortices) reconstruct edges more boldly. We illustrate variable convergence of these simple neural networks and relate learnability of an image to its singular value decomposition entropy (SVD-entropy) of the image. We also provide heuristic experimental results. With thresholding, we achieve robust reconstruction of various disjoint datasets. Our work is favorable for future real-time low-SWaP hybrid vision: we reconstruct images on a 15W laptop CPU with 15k fps: faster by a factor of 3 than previously reported results and 3 orders of magnitude faster than convolutional neural networks.

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Fourier single-pixel imaging using fewer illumination patterns

Cited by 42 publications

References 29 publications

Complementary Fourier Single-Pixel Imaging

Complementary Fourier Single-Pixel Imaging

Transmissive Single-Pixel Microscopic Imaging through Scattering Media

Toward simple, generalizable neural networks with universal training for low-SWaP hybrid vision

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