scholarly journals Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy

2014 ◽  
Vol 5 ◽  
pp. 2016-2025 ◽  
Author(s):  
Torsten Bölke ◽  
Lisa Krapf ◽  
Regina Orzekowsky-Schroeder ◽  
Tobias Vossmeyer ◽  
Jelena Dimitrijevic ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Ground Truth ◽  
Adaptive Method ◽  
Fluorescent Nanoparticles ◽  
Noise Characteristics ◽  
Mucosal Tissues ◽  
Noise Structure ◽  
Non Local ◽  
Fine Structures ◽  
Data Adaptive

Intravital 2-photon microscopy of mucosal membranes across which nanoparticles enter the organism typically generates noisy images. Because the noise results from the random statistics of only very few photons detected per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The algorithm exploits both local and non-local redundancy of the underlying ground-truth signal to reduce noise. Our approach automatically adapts the strength of noise suppression in a data-adaptive way by using a Bayesian network. The results show that the specific adaption to both signal and noise characteristics improves the preservation of fine structures such as nanoparticles while less artefacts were produced as compared to reference algorithms. Our method is applicable to other imaging modalities as well, provided the specific noise characteristics are known and taken into account.

Directional Validation of Wave Predictions*

2007 ◽  
Vol 24 (3) ◽  
pp. 504-520 ◽  
Author(s):  
W. Erick Rogers ◽  
David W. C. Wang
Keyword(s):  
Ground Truth ◽  
Wave Model ◽  
Peak Frequency ◽  
Adaptive Method ◽  
Parametric Model ◽  
Spectral Peak ◽  
Systematic Bias ◽  
Data Adaptive ◽  
Interpretation Of Results

Abstract A methodology for quantitative, directional validation of a long-term wave model hindcast is described and applied. Buoy observations are used as ground truth and the method does not require the application of a parametric model or data-adaptive method to the observations. Four frequency ranges, relative to the peak frequency, are considered. The validation of the hindcast does not suggest any systematic bias in predictions of directional spreading at or above the spectral peak. Idealized simulations are presented to aid in the interpretation of results.

A stable data adaptive method for matched‐field array processing in acoustic waveguides

1989 ◽  
Vol 85 (S1) ◽  
pp. S17-S17
Author(s):  
C. L. Byrne ◽  
R. I. Brent ◽  
C. Feuillade ◽  
D. R. DelBalzo
Keyword(s):  
Array Processing ◽  
Adaptive Method ◽  
Acoustic Waveguides ◽  
Data Adaptive

scholarly journals The Statistical Properties of Sea Ice Velocity Fields

2017 ◽  
Vol 30 (13) ◽  
pp. 4873-4881 ◽  
Author(s):  
Sahil Agarwal ◽  
John S. Wettlaufer
Keyword(s):  
Sea Ice ◽  
White Noise ◽  
Time Scales ◽  
Velocity Fields ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Noise Characteristics ◽  
Noise Structure ◽  
Spatial Correlation Structure ◽  
Ice Velocity

By arguing that the surface pressure field over the Arctic Ocean can be treated as an isotropic, stationary, homogeneous, Gaussian random field, Thorndike estimated a number of covariance functions from two years of data (1979 and 1980). Given the active interest in changes of general circulation quantities and indices in the polar regions during the recent few decades, the spatial correlations in sea ice velocity fields are of particular interest. It is thus natural to ask, “How persistent are these correlations?” To this end, a multifractal stochastic treatment is developed to analyze observed Arctic sea ice velocity fields from satellites and buoys for the period 1978–2015. Since it was previously found that the Arctic equivalent ice extent (EIE) has a white noise structure on annual to biannual time scales, the connection between EIE and ice motion is assessed. The long-term stationarity of the spatial correlation structure of the velocity fields and the robustness of their white noise structure on multiple time scales is demonstrated; these factors (i) combine to explain the white noise characteristics of the EIE on annual to biannual time scales and (ii) explain why the fluctuations in the ice velocity are proportional to fluctuations in the geostrophic winds on time scales of days to months. Moreover, it is shown that the statistical structure of these two quantities is commensurate from days to years, which may be related to the increasing prevalence of free drift in the ice pack.

Noise suppression for energy-resolved CT using similarity-based non-local filtration

2016 ◽  
Author(s):  
Joe Harms ◽  
Tonghe Wang ◽  
Michael Petrongolo ◽  
Lei Zhu
Keyword(s):  
Noise Suppression ◽  
Non Local

scholarly journals In situ measurements of directional wave spectra from an unmanned aerial system

2021 ◽  
Author(s):  
Clarence Collins ◽  
Katherine Brodie
Keyword(s):  
Single Point ◽  
Field Research ◽  
Ocean Waves ◽  
Adaptive Method ◽  
Technical Note ◽  
Sea Surface ◽  
Unmanned Aerial System ◽  
Frequency Spectra ◽  
Directional Spectrum ◽  
Data Adaptive

This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the ability to measure the directional-frequency spectrum of sea surface waves based on the motion of a floating unmanned aerial system (UAS). The UAS used in this effort was custom built and designed to land on and take off from the sea surface. It was deployed in the vicinity of an operational wave sensor, the 8 m* array, at the US Army Engineer Research and Development Center (ERDC), Field Research Facility (FRF) in Duck, NC. While on the sea surface, an inertial navigation system (INS) recorded the response of the UAS to the incoming ocean waves. Two different INS signals were used to calculate one-dimensional (1D) frequency spectra and compared against the 8 m array. Two-dimensional (2D) directional-frequency spectra were calculated from INS data using traditional single-point-triplet analysis and a data adaptive method. The directional spectrum compared favorably against the 8 m array.

scholarly journals Speckle noise suppression method using multi-azimuth SAR images.

2021 ◽  
Vol 2083 (3) ◽  
pp. 032053
Author(s):  
Yingru Shi ◽  
Yang Liu ◽  
Peili Xi ◽  
Wei Yang ◽  
Hongcheng Zeng
Keyword(s):  
Noise Suppression ◽  
Speckle Noise ◽  
Image Sequences ◽  
Sar Images ◽  
Radar Images ◽  
Local Means ◽  
Structural Details ◽  
Novel Method ◽  
Non Local ◽  
Suppression Method

Abstract Synthetic aperture radar images play an important role in military and civilian fields, but the presence of speckle noise has an impact on subsequent tasks such as target detection and target interpretation. With the development of multi-azimuth observation mode, the obtained multi-azimuth image sequences have high similarities. Therefore, combined with multi-azimuth image sequences, a novel method of SAR image speckle noise suppression based on clustering is proposed in this paper. In this method, multi-azimuth joint filtering framework based on two-level filtering is proposed, in which pre-filtering for single image and joint filtering based on Non-local Means algorithm for multi-azimuth image are used to suppress the noise. And k-means clustering is used to optimize the search area in the multi-azimuth joint filtering, so as to effectively suppress speckle noise while retaining structural details.

scholarly journals Deep-Learning-Based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient Recalled Echo (qGRE) MRI metrics associated with Human Brain Neuronal Structure and Hemodynamic Properties

2021 ◽  
Author(s):  
Sayan Kahali ◽  
Satya V.V.N. Kothapalli ◽  
Xiaojian Xu ◽  
Ulugbek S Kamilov ◽  
Dmitriy A Yablonskiy
Keyword(s):  
Magnetic Field ◽  
Deep Learning ◽  
Noise Suppression ◽  
Ground Truth ◽  
Least Square ◽  
Biophysical Model ◽  
Neuronal Structure ◽  
Brain Maps ◽  
Computational Speed ◽  
Phase Images

Purpose: To introduce a Deep-Learning-Based Accelerated and Noise-Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular-specific, and hemodynamic-specific, from Gradient-Recalled-Echo (GRE) MRI data with multiple gradient-recalled echoes. Methods: DANSE method adapts supervised learning paradigm to train a convolutional neural network for robust estimation of and maps free from the adverse effects of macroscopic (B0) magnetic field inhomogeneities directly from the GRE magnitude images without utilizing phase images. The corresponding ground-truth maps were generated by means of a voxel-by-voxel fitting of a previously-developed biophysical quantitative GRE (qGRE) model accounting for tissue, hemodynamic and -inhomogeneities contributions to GRE signal with multiple gradient echoes using nonlinear least square (NLLS) algorithm. Results: We show that the DANSE model efficiently estimates the aforementioned brain maps and preserves all features of NLLS approach with significant improvements including noise-suppression and computation speed (from many hours to seconds). The noise-suppression feature of DANSE is especially prominent for data with SNR characteristic for typical GRE data (SNR~50), where DANSE-generated and maps had three times smaller errors than that of NLLS method. Conclusions: DANSE method enables fast reconstruction of magnetic-field-inhomogeneity-free and noise-suppressed quantitative qGRE brain maps. DANSE method does not require any information about field inhomogeneities during application. It exploits spatial patterns in the qGRE MRI data and previously-gained knowledge from the biophysical model, thus producing clean brain maps even in the environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications.

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