Hybrid Speckle-Pattern Compressive Computational Ghost Imaging

Cheng, 周成 Zhou; Heyan, 黄贺艳 Huang; Bing, 刘兵 Liu; Lijun, 宋立军 Song

doi:10.3788/aos201636.0911001

Cited by 6 publications

(4 citation statements)

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“…In terms of random matrix optimization, methods such as multi-scale 24 – 26 , orthogonalization 27 , sparse constraints 28 , 29 , and singular value decomposition 30 , 31 have been proposed one after another, greatly improving imaging quality and algorithm performance. However, their computational and implementation complexity is still high, and further in-depth optimization is still needed 32 – 34 .…”

Section: Introductionmentioning

confidence: 99%

Fast high quality computational ghost imaging based on saliency variable sampling detection

Liu,

Hu,

et al. 2024

Sci Rep

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Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate $$0.76\%$$ 0.76 % ) measurement times, the reconstructed image of the method still has the target features. Moreover, the $$2160\times 4096$$ 2160 × 4096 (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of $$0.11\%$$ 0.11 % . This method has great potential value in real-time detection and tracking, biological imaging and other fields.

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Section: Introductionmentioning

confidence: 99%

Fast high quality computational ghost imaging based on saliency variable sampling detection

Liu,

Hu,

et al. 2024

Sci Rep

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“…Chen et al [25] presented the possibility of generating multi-correlation-scale speckle patterns to reconstruct the ghost image. Zhou et al [26] proposed a method to improve the quality of ghost images using a hybrid speckle pattern. In this method, the higher and lower resolution areas of the object could be detected by automatically identifying complex objects composed of different resolution scales.…”

Section: Introductionmentioning

confidence: 99%

Image-Enhanced Pseudo-Thermal Ghost Imaging with Hybrid Speckle Pattern

Tian

Sun

et al. 2023

Photonics

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In this study, the influence of hybrid speckle patterns on the contrast-to-noise ratio (CNR) and resolution in pseudo-thermal ghost imaging (PGI) was examined based on the object dimensions in the macroscopic and microscopic regimes. This research shows that an enhanced scaling of the ghost image CNR and resolution from that of the hybrid speckle pattern was observed with the increase in speckle size for a macroscopic object, compared with the use of single-size speckle patterns. For microscopic objects, the hybrid speckle pattern also offered the advantage of retrieving ghost images even if the CNR followed the same trend as the resolution. These results were verified using two different slits with the same transmitted area. In addition, the numerical analysis revealed that the interference of the hybrid speckle pattern was the major factor for a better CNR. Based on these findings, the novel hybrid speckle pattern found in this research provides a possible way for future experiments in PGI to regulate hybrid speckle patterns to obtain a better ghost image quality.

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“…Recently, a variety of advanced basis illumination pat-terns based on ghost imaging methods have been proposed to enhance the imaging quality. [22][23][24][25][26][27][28][29][30][31][32][33][34] For example, Hadamard basis based ghost imaging and Fourier basis based ghost imaging, [22][23][24][25][26][27][28][29][30][31] which constitute a complete orthogonal set, can enhance the imaging quality and shorten the reconstruction time. However, if the speckle patterns are random, there is a high constant background that degrades the imaging quality.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal-triangular decomposition ghost imaging

Liu¹,

Wang²,

Zhao³

2022

Chinese Phys. B

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Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. However, there are still some obstacles for reconstructing images with high quality, especially in the case the orthogonal measurement matrix is impossible to construct. In this paper, we propose a new scheme based on orthogonaltriangular (QR) decomposition, named QR decomposition ghost imaging (QRGI) to reconstruct a better image with good quality. In the scheme, we can change the randomly non-orthogonal measurement matrix into orthonormal matrix by performing QR decomposition in two cases. (1) When the random measurement matrix is square, it can be first decomposed into an orthogonal matrix Q and an upper triangular matrix R. Then let the off-diagonal values of R equal to 0.0, the diagonal elements of R equal to a constant k, where k is the average of all values of the main diagonal, so the resulting measurement matrix can be obtained. (2) When the random measurement matrix is with full rank, we first compute its transpose, and followed with above QR operation. Finally, the image of the object can be reconstructed by correlating the new measurement matrix and corresponding bucket values. Both experimental and simulation results verify the feasibility of the proposed QRGI scheme. Moreover, the results also show that the proposed QRGI scheme could improve the imaging quality comparing to traditional GI (TGI) and differential GI (DGI). Besides, in comparison with the singular value decomposition ghost imaging (SVDGI), the imaging quality and the reconstruction time by using QRGI are similar to those by using SVDGI, while the computing time (the time-consuming on the light patterns computation) is substantially shortened.

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Hybrid Speckle-Pattern Compressive Computational Ghost Imaging

Cited by 6 publications

References 0 publications

Fast high quality computational ghost imaging based on saliency variable sampling detection

Fast high quality computational ghost imaging based on saliency variable sampling detection

Image-Enhanced Pseudo-Thermal Ghost Imaging with Hybrid Speckle Pattern

Orthogonal-triangular decomposition ghost imaging

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