Edge detection based on joint iteration ghost imaging

Zhou, Cheng; Wang, Gangcheng; Huang, Heyan; Song, Lijun; Xue, Kang

doi:10.1364/oe.27.027295

Cited by 33 publications

(10 citation statements)

References 50 publications

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“…The crops were then subjected to our morphological feature-based pipeline that processes single images from disease or control neurons. Here we are examining the perimeter of the cell based on contour ellipse 47 . This simplified dataset consists of a minimal category of experimental variables used to run power simulation analyses with known ICC values.…”

Section: Introductionmentioning

confidence: 99%

RMeDPower for Biology: guiding design, experimental structure and analyses of repeated measures data for biological studies

Shin

Kaye

Amirani

et al. 2022

Preprint

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Reproducibility is science has plagued efforts to understand biology at both basic and biomedical and preclinical research levels. Poor experimental design and execution can result in datasets that are improperly powered to produce rigorous and reproducible results. In order to help biologists better model their data, here we present a statistical package called RMeDPower in R, which is a complete package of statistical tools that allow a scientist to understand the effect size and variance contribution of a set of variables one has within a dataset to a given response. RMeDPower can estimate the effect size of variables within an experiment based on an initial pilot dataset. In this way, RMeDPower can inform the user how to predict the scope, dimension and size of biological data needed for a particular experimental design. RMeDPower employs a generalized linear mixed model (LMM) -based power analysis, specifically targeting cell culture-based biological experimental designs. This package simulates experiments based on user-provided experimental design related variables, such as experiments, plates, and cell lines as random effects variables. This package not only allows us to use pilot data to estimate variance components for power simulation, it also accepts a set of variance components, which is an estimation of variance of the random effects linked to experimental variables and transformed into Intra-class Correlation Coefficients (ICC), as input which is precalculated from different data sets. The latter case is suitable when pilot data has an insufficient number of replications of experimental variables to directly estimate associated variance components. RMeDPower is a powerful package that any scientist or cell biologist can use to determine if a dataset is adequately powered for each experiment and then model accordingly.

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Section: Introductionmentioning

confidence: 99%

RMeDPower for Biology: guiding design, experimental structure and analyses of repeated measures data for biological studies

Shin

Kaye

Amirani

et al. 2022

Preprint

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“…Most recently, with the wide application of image filtering method in the field of image processing and image denoising, the combination of the image filtering algorithm and compressed sensing algorithm has become one research focus. This kind of combination algorithm can reconstruct an image with higher quality than the other algorithms and is widely used in CSGI system [28], [29]. Inspired by the combination algorithm, this paper proposes a combination algorithm of the split Bregman iteration [30], [31] and BM3D filter [32], [33] to reconstruct target intensity images from the data measured by the bucket detector in the CSGI system.…”

Section: Introductionmentioning

confidence: 99%

The Reconstruction Quality Improvement of Single-Pixel Imaging via Modified Split-Bregman Iteration

Yang

et al. 2020

IEEE Access

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A modified Split-Bregman iteration method is proposed to reconstruct the image of target in computational ghost imaging. Different from the conventional Split-Bregman iteration, each reconstruction result of Split-Bregman iteration is denoised by BM3D filter in the proposed, which can help reduce the noise of the final reconstructed result effectively. The method has high quality reconstruction effectiveness and is able to retain the edge and texture detail information well. The numerical simulation and experiment results verify the feasibility of the proposed method. Comparing with other methods qualitatively and quantitatively, our method has high performance in reconstructing image of target. INDEX TERMS Ghost imaging, high reconstruction quality, iteration with filtering method, modified Split-Bregman iteration.

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“…Recently, CGI based on Hadamard coding has attracted more and more researchers' attention, and has aroused in creasing interest in application like microscopic imaging [7,8], X-ray imaging [9][10][11][12][13], three-dimensional imaging [14][15][16], methane gas detection [17] and so on [18][19][20][21][22]. The most important reason is that Hadamard derived patterns are orthogonal to each other, particularly the final reconstruction quality of CGI is significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Hadamard `Pipeline' Coding Computational Ghost Imaging

Zhou,

Zhao,

Huang

et al. 2019

Preprint

Self Cite

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The Hadamard matrix with orthogonality is a more important modulation matrix for computational ghost imaging (CGI), especially its optimized Hadamard matrix. However, as far as we know, little mention has been paid to efficient and convenient Hadamard matrix generation for CGI. The existing methods are to reconstruct any row of Hadamard matrix into two-dimensional matrix and then optimize it. In this work, we propose a Hadamard 'pipeline' coding computational ghost imaging approach, which can directly generate two-dimensional Hadamard derived pattern and Hadamard optimization sequence, whereby both the memory consumption and the complexity of coding implementation for CGI can be significantly reduced. The optimization method of commonly used hadamard optimization sequence implementation is also discussed. This method provides a new approach for Hadamard sequence optimization and ghost imaging applications.

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Edge detection based on joint iteration ghost imaging

Cited by 33 publications

References 50 publications

RMeDPower for Biology: guiding design, experimental structure and analyses of repeated measures data for biological studies

RMeDPower for Biology: guiding design, experimental structure and analyses of repeated measures data for biological studies

The Reconstruction Quality Improvement of Single-Pixel Imaging via Modified Split-Bregman Iteration

Hadamard `Pipeline' Coding Computational Ghost Imaging

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