Image quality recovery in binary ghost imaging by adding random noise

Li, Junhui; Yang, Dongyue; Luo, Bin; Wu, Guohua; Yin, Longfei; Guo, Hong

doi:10.1364/ol.42.001640

Cited by 30 publications

(12 citation statements)

References 11 publications

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“…where δ is the quantizer step size, and the floor operator • returns the largest integer that does not exceed its argument [27]. In this paper, δ is satisfies the condition that max(I) − min(I) ≤ 2 n δ.…”

Section: Simulationsmentioning

confidence: 99%

“…In this paper, we show that using speckles with low bit depths to recover the object, which means the requirement of cameras' hardware and the image storage space can be reduced. We implement a conventional speckle auto-correlation method to reconstruct the object's image and apply uniform quantization [27], [28] on the raw data to achieve sampling compression of bit depth. When the bit depth of the quantized speckle increase, the quality of the recovered image can increase.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Imaging and Tracking Through Scattering Medium With Low Bit Depth Speckle

Sun

Zhao

et al. 2020

IEEE Photonics J.

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Optical imaging/tracking through scattering medium is a difficult challenge. We demonstrate a method using speckles with multiple bit depths to image objects through scattering medium. The image of the object can be reconstructed even if the speckles are binary. Speckles with different bit depths are obtained by quantifying the original speckle. And we discuss the application of binarized speckles in tracking moving object. The binarized speckles can be used to recover the track with high accuracy. The range of the object's motion can be much larger than the range of memory effect, as long as the object's size and the moving step are within the range of memory effect. Imaging and tracking with low bit depth have the potential to reduce the hardware requirements of camera and storage space of images.

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Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Imaging and Tracking Through Scattering Medium With Low Bit Depth Speckle

Sun

Zhao

et al. 2020

IEEE Photonics J.

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show abstract

“…Compromisingly, a large number of repeated measurements are required for reconstructing a high quality image [5][6][7], which has become a major drawback preventing GI from practical applications, especially real-time tasks, even with the help of compressive sensing technique [8]. Considering this, reducing the dynamic range of detectors or recording measurements with less bits, even 1-bit, would speed up GI process significantly when there are less data to be sampled, transported, stored, and calculated [9]. In fact, it is even more suitable for computational GI [10], where the reference camera is replaced by a spatial light modulator, thus the sampling process of reference camera is equivalent to the pattern modulation of spatial light modulator.…”

Section: Introductionmentioning

confidence: 99%

“…When we digitalize the signal into 1-bit, we create at each output a quantization error: the difference between the original signal and the binarization threshold. This quantization error does harm the image quality of GI [9]. Here comes the question -given a binary sampling GI scenario with certain characteristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

Ghost Imaging with the Optimal Binary Sampling

Yang,

Wu,

Luo

et al. 2020

Preprint

Self Cite

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To extract the maximum information about the object from a series of binary samples in ghost imaging applications, we propose and demonstrate a framework for optimizing the performance of ghost imaging with binary sampling to approach the results without binarization. The method is based on maximizing the information content of the signal arm detection, by formulating and solving the appropriate parameter estimation problem -finding the binarization threshold that would yield the reconstructed image with optimal Fisher information properties. Applying the 1-bit quantized Poisson statistics to a ghost-imaging model with pseudo-thermal light, we derive the fundamental limit, i.e., the Cramér-Rao lower bound, as the benchmark for the evaluation of the accuracy of the estimator. Our theoertical model and experimental results suggest that, with the optimal binarization threshold, coincident with the statistical mean of all bucket samples, and large number of measurements, the performance of binary sampling GI can approach that of the ordinary one without binarization.

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“…To test the feasibility of the OCGI method, we run a simulation firstly in ideal conditions without environmental and system noise. To better judge the performance of various methods, i.e., the OCGI, OWGI, WGI, and PGI, we utilize four evaluating indicators of image quality, i.e., CNR, MSE, PSNR, and CC [18,[21][22][23]:…”

mentioning

confidence: 99%

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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Image quality recovery in binary ghost imaging by adding random noise

Cited by 30 publications

References 11 publications

Imaging and Tracking Through Scattering Medium With Low Bit Depth Speckle

Imaging and Tracking Through Scattering Medium With Low Bit Depth Speckle

Ghost Imaging with the Optimal Binary Sampling

Sub-Nyquist computational ghost imaging with orthonormalized colored noise pattern

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