Anti-interference Computational Ghost Imaging with Pink Noise Speckle Patterns

Nie, Xiaoyu; Yang, Fan; Liu, Xiangpei; Zhao, Xingchen; Nessler, Reed; Peng, Tao; Zubairy, M. Suhail; Scully, Marlan O.

doi:10.48550/arxiv.2009.14390

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…As shown in Fig. 4, the OCGI, similar to the PGI, gives a stronger signal in the low sample rate domain, as demonstrated in our previous study [20]. The OCGI always has the best image quality.…”

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confidence: 78%

“…The spatial distribution of the light field amplitude is Gaussian, and the phase associated with the field amplitude is random. We recently developed a method to generate the so-called colored noise speckle pattern for CGI by customizing the speckle patterns' power spectrum density [20]. Unlike white noise, colored noise generally has non-zero cross-correlation between neighborhood pixels.…”

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confidence: 99%

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Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern

Nie,

Zhao,

Peng

et al. 2020

Preprint

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Computational ghost imaging generally requires a large number of pattern illumination to obtain a high-quality image. The colored noise speckle pattern was recently proposed to substitute the white noise pattern in a variety of noisy environments and gave a significant signal-to-noise ratio enhancement even with a limited number of patterns. We propose and experimentally demonstrate here an orthonormalization approach based on the colored noise patterns to achieve sub-Nyquist computational ghost imaging. We tested the reconstructed image in quality indicators such as the contrast-to-noise ratio, the mean square error, the peak signal to noise ratio, and the correlation coefficient. The results suggest that our method can provide high-quality images while using a sampling ratio an order lower than the conventional methods.Computational ghost imaging (CGI) [1-3], an ameliorated scheme on traditional ghost imaging (GI) [4][5][6][7], owns the ability to reconstruct the object via single bucket detector. CGI also grants advantages in an expanding range of non-conventional applications such as wide spectrum imaging [8,9] and depth mapping [10,11]. It also finds application to various fields, such as temporal imaging [3], X-ray imaging [12], remote sensing [13], etc.. However, its sampling number is usually comparable to the total number of pixels in the speckle pattern to keep good imaging quality. Thus, it is time-consuming and resource-intensive Besides, it is only suitable for static object reconstruction.Various methods have been proposed to overcome this problem [14][15][16][17][18][19]. One typical and effective way is the orthonormalization method [18]. This method introduces a data post-processing algorithm to improve the reconstructing process in a GI system with pseudo-thermal light. The required sampling number is reduced by applying the Gram-Schmidt process on the noise patterns and intensity sequence collected by the bucket detector However, such a method is sensitive to noise, and the image quality is not even comparable with standard CGI when the pattern number is large enough. Traditionally, Gaussian white noise speckle pattern is used for GI. The spatial distribution of the light field amplitude is Gaussian, and the phase associated with the field amplitude is random. We recently developed a method to generate the so-called colored noise speckle pattern for CGI by customizing the speckle patterns' power spectrum density [20]. Unlike white noise, colored noise generally has non-zero cross-correlation between neighborhood pixels. Sub-Rayleigh imaging was demonstrated with the blue noise pattern, which has negative cross-correlation between two adjacent pixels. The pink noise pattern allowed us to image in a variety of noisy environments.This letter introduces a novel method for combining the colored noise and Orthonormalization methods to substantially reduce the number of sampling and overcome the drawback of pink noise patterns. We also compare the orthonormalization colored noise GI (OCGI) with orthon...

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confidence: 78%

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confidence: 99%

Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern

Nie,

Zhao,

Peng

et al. 2020

Preprint

Self Cite

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“…When the target moves too fast, the equivalent static sampling number will drop to fairly low so that the image quality will be destroyed, especially its signal-to-noise ratio (SNR) [15]. Recently, we developed a method to generate the so-called pink noise speckle pattern for CGI by customizing the power spectrum density of the speckle patterns [27]. Unlike white noise, the CGI with pink noise is robust to a variety of noisy environments.…”

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Moving Object Captured with Pink Noise Pattern in Computational Ghost Imaging

Nie¹,

Zhao²,

Peng³

et al. 2020

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We develop and experimentally demonstrate an imaging method based on the pink noise pattern in the computational ghost imaging (CGI) system, which has a strong ability to photograph moving objects. To examine its unique ability and scope of application, the object oscillates with variable amplitude in horizontal axis, and the result via commonly used white noise are also measured as a comparison. We show that our method can image the object when the white noise method fails. In addition, our method uses less number of patterns, and enhances the signal-to-noise ratio (SNR) to a great extent.

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Anti-interference Computational Ghost Imaging with Pink Noise Speckle Patterns

Cited by 2 publications

References 32 publications

Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern

Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern

Moving Object Captured with Pink Noise Pattern in Computational Ghost Imaging

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