Single-Pixel Imaging with Origami Pattern Construction

Yu, Wen-Kai; Liu, Yiming

doi:10.3390/s19235135

Cited by 33 publications

(17 citation statements)

References 37 publications

(51 reference statements)

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“…Figure 2 shows the reconstructed images when the number of measurements is 1,024. Random modulation patterns are generated with random numbers; for the basis pattern, we used a state-of-the-art pattern called Origami, which was proposed in [8]. These patterns are binary.…”

Section: Simulation and Experimentsmentioning

confidence: 99%

“…Our concept follows that of a previous study [22], whereby the learning of modulation patterns is incorporated into a deep neural network by considering them as network parameters. Unlike spatial frequency-based patterns [7], [8], the proposed modulation patterns improve image quality while retaining robustness against noise and high resolution, even for undersampling. We can therefore achieve ghost imaging with an improved image quality while retaining high resolution and noise robustness using the proposed method.…”

Section: Introductionmentioning

confidence: 99%

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Optimized Binary Patterns by Gradient Descent for Ghost Imaging

et al. 2021

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Ghost imaging reconstructs images using a single-element photodetector; it performs imaging by illuminating an object with binary modulation patterns. This technique has various advantages, including a wide wavelength, noise robustness, and high measurement sensitivity. However, one challenge is the low image quality in the undersampling. The examination of modulation patterns is intended to solve this issue. In ghost imaging, randomly generated or basis patterns have been studied as modulation patterns; however, in undersampling, random patterns exhibit noise robustness but low image quality, whereas basis patterns exhibit high image quality but are sensitive to noise and low resolution. Thus, ghost imaging requires patterns that simultaneously achieve high image quality, resolution, and noise robustness. This study proposes a method of pattern optimization using gradient descent and a binarization method to further improve image quality. Numerical simulation and experimental results show that the proposed approach offers high image quality, high resolution, and robustness to noise.

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Section: Simulation and Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Binary Patterns by Gradient Descent for Ghost Imaging

et al. 2021

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“…Several ordering techniques [17][18][19][20][21] have been proposed successively to study the effect of the deterministic pattern construction on reconstruction performance. In "Russian dolls" (RD) ordering [17], the Hadamard basis is catalogued into four quarters, the first quarter is further split into four new quarters, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Single-pixel imaging based on weight sort of the Hadamard basis

Yu¹,

Chen²,

Wei³

et al. 2022

Preprint

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Single-pixel imaging (SPI) is very popular in subsampling applications, but the random measurement matrices it typically uses will lead to measurement blindness as well as difficulties in calculation and storage, and will also limit the further reduction in sampling rate. The deterministic Hadamard basis has become an alternative choice due to its orthogonality and structural characteristics. There is evidence that sorting the Hadamard basis is beneficial to further reduce the sampling rate, thus many orderings have emerged, but their relations remain unclear and lack a unified theory. Given this, here we specially propose a concept named selection history, which can record the Hadamard spatial folding process, and build a model based on it to reveal the formation mechanisms of different orderings and to deduce the mutual conversion relationship among them. Then, a weight ordering of the Hadamard basis is proposed. Both numerical simulation and experimental results have demonstrated that with this weight sort technique, the sampling rate, reconstruction time and matrix memory consumption are greatly reduced in comparison to traditional sorting methods. Therefore, we believe that this method may pave the way for real-time single-pixel imaging.

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“…The Fourier spectrum of the desired object image can be acquired by using Fourier basis patterns for structured illumination or structured detection. Compared with other basis-scan single-pixel imaging methods (such as, Hadamard [23][24][25][26][27], DCT [28]), FSI has been proven more data-efficient when the differential method of measurement is employed [29]. Specifically, FSI with 3-step phase shifting can reconstruct a lossless image in a differential-measurement manner taking as many measurements as 1.5 times the number of image pixels.…”

Section: Introductionmentioning

confidence: 99%

“…We note that the research on basis patterns ordering is a hot spot in single-pixel imaging, because an optimal sampling strategy enables images of unchanged quality to be reconstructed from the least single-pixel measurements and therefore shortest data acquisition time. For example, Russian doll [23], cake-cutting [24], origami [25], and total variation ascending orderings [26] were recently proposed for Hadamard single-pixel imaging.…”

Section: Introductionmentioning

confidence: 99%

Efficient Fourier Single-Pixel Imaging with Gaussian Random Sampling

Qiu

Guo

et al. 2021

Photonics

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Fourier single-pixel imaging (FSI) is a branch of single-pixel imaging techniques. It allows any image to be reconstructed by acquiring its Fourier spectrum by using a single-pixel detector. FSI uses Fourier basis patterns for structured illumination or structured detection to acquire the Fourier spectrum of image. However, the spatial resolution of the reconstructed image mainly depends on the number of Fourier coefficients sampled. The reconstruction of a high-resolution image typically requires a number of Fourier coefficients to be sampled. Consequently, a large number of single-pixel measurements lead to a long data acquisition time, resulting in imaging of a dynamic scene challenging. Here we propose a new sampling strategy for FSI. It allows FSI to reconstruct a clear and sharp image with a reduced number of measurements. The key to the proposed sampling strategy is to perform a density-varying sampling in the Fourier space and, more importantly, the density with respect to the importance of Fourier coefficients is subject to a one-dimensional Gaussian function. The final image is reconstructed from the undersampled Fourier spectrum through compressive sensing. We experimentally demonstrate the proposed method is able to reconstruct a sharp and clear image of 256 × 256 pixels with a sampling ratio of 10%. The proposed method enables fast single-pixel imaging and provides a new approach for efficient spatial information acquisition.

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Single-Pixel Imaging with Origami Pattern Construction

Cited by 33 publications

References 37 publications

Optimized Binary Patterns by Gradient Descent for Ghost Imaging

Optimized Binary Patterns by Gradient Descent for Ghost Imaging

Single-pixel imaging based on weight sort of the Hadamard basis

Efficient Fourier Single-Pixel Imaging with Gaussian Random Sampling

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