scholarly journals Poisson-Gaussian noise parameter estimation in fluorescence microscopy imaging

2012 ◽  
Author(s):  
Anna Jezierska ◽  
Hugues Talbot ◽  
Caroline Chaux ◽  
Jean-Christophe Pesquet ◽  
Gilbert Engler
Keyword(s):  
Parameter Estimation ◽  
Fluorescence Microscopy ◽  
Gaussian Noise ◽  
Microscopy Imaging ◽  
Noise Parameter

scholarly journals Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

2021 ◽  
Author(s):  
Varun Mannam ◽  
Yide Zhang ◽  
Yinhao Zhu ◽  
Evan Nichols ◽  
Qingfei Wang ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Image Denoising ◽  
Gaussian Noise ◽  
High Performance ◽  
Signal To Noise Ratio ◽  
Analytical Techniques ◽  
Training Data ◽  
Superior Performance ◽  
Microscopy Imaging ◽  
Nyquist Noise

Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, analytical techniques to estimate a denoised image from a single frame are either computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson-Nyquist noise, and therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs instant image denoising (within tens of milliseconds) with superior performance (~8.1 dB SNR improvement) compared to the conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise and contrast from the training data and consistently achieves high performance (>8dB) denoising in less time than other fluorescence microscopy denoising methods.

Fluorescence Microscopy Imaging of Bone for Automated Histomorphometry

2002 ◽  
Vol 8 (5) ◽  
pp. 847-852 ◽  
Author(s):  
Ivan Martin ◽  
Maddalena Mastrogiacomo ◽  
Gianluca De Leo ◽  
Anita Muraglia ◽  
Francesco Beltrame ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Microscopy Imaging

Co-localization analysis in fluorescence microscopy via maximum entropy copula

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zahra Amini Farsani ◽  
Volker J. Schmid
Keyword(s):  
Fluorescence Microscopy ◽  
Maximum Entropy ◽  
Probability Distributions ◽  
Real Data ◽  
Bivariate Distribution ◽  
Entropy Method ◽  
Gaussian Copula ◽  
Microscopy Imaging ◽  
High Background ◽  
Localization Analysis

AbstractCo-localization analysis is a popular method for quantitative analysis in fluorescence microscopy imaging. The localization of marked proteins in the cell nucleus allows a deep insight into biological processes in the nucleus. Several metrics have been developed for measuring the co-localization of two markers, however, they depend on subjective thresholding of background and the assumption of linearity. We propose a robust method to estimate the bivariate distribution function of two color channels. From this, we can quantify their co- or anti-colocalization. The proposed method is a combination of the Maximum Entropy Method (MEM) and a Gaussian Copula, which we call the Maximum Entropy Copula (MEC). This new method can measure the spatial and nonlinear correlation of signals to determine the marker colocalization in fluorescence microscopy images. The proposed method is compared with MEM for bivariate probability distributions. The new colocalization metric is validated on simulated and real data. The results show that MEC can determine co- and anti-colocalization even in high background settings. MEC can, therefore, be used as a robust tool for colocalization analysis.

scholarly journals Atomic force and fluorescence microscopy imaging of cardiomyocyte plasma membrane, insulin receptor and caveolin‐3

2009 ◽  
Vol 23 (S1) ◽  
Author(s):  
Elizabeth G McAndrew ◽  
Jianjing Xue ◽  
Loren E Wold ◽  
James Vesenka ◽  
Amy J Davidoff
Keyword(s):  
Plasma Membrane ◽  
Fluorescence Microscopy ◽  
Insulin Receptor ◽  
Microscopy Imaging ◽  
Atomic Force

scholarly journals Scattered light fluorescence microscopy: imaging through turbid layers

2010 ◽  
Vol 35 (8) ◽  
pp. 1245 ◽  
Author(s):  
Ivo M. Vellekoop ◽  
Christof M. Aegerter
Keyword(s):  
Fluorescence Microscopy ◽  
Scattered Light ◽  
Microscopy Imaging ◽  
Light Fluorescence

Parameter Estimation of Co-Channel AIS Signal Using Ambiguity Function

2014 ◽  
Vol 644-650 ◽  
pp. 4035-4039
Author(s):  
Hao Su Zhou ◽  
Jian Xin Wang
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Reference Signal ◽  
Two Dimensions ◽  
Ambiguity Function ◽  
Weak Signal ◽  
Strong Signal ◽  
Estimated Parameters ◽  
Simulation Results

A new data-aided algorithm for parameter estimation of the co-channel AIS signal transmitted over the additive white Gaussian noise channel is proposed in this paper. The co-channel signal consists of a strong signal with high power and a weak signal with low power. The parameters of the strong signal are estimated by searching the ambiguity function of the co-channel signal in two dimensions. A reference signal is therefore reconstructed with the estimated parameters and the aided data. By removing the ambiguity function of the reconstructed reference signal from that of the original co-channel signal, a new co-channel signal ambiguity function is obtained, from which the parameters of the weak signal are estimated. The simulation results illustrate that the proposed algorithm can estimate the parameters of the co-channel AIS signal effectively.

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