scholarly journals Extracting Filaments Based on Morphology Components Analysis from Radio Astronomical Images

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
M. Zhu ◽  
W. Liu ◽  
B. Y. Wang ◽  
M. F. Zhang ◽  
W. W. Tian ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Signal To Noise Ratio ◽  
Local Information ◽  
Astronomical Image ◽  
Translation Function ◽  
Extraction Algorithm ◽  
Components Analysis ◽  
Astronomical Images ◽  
Wavelet Translation ◽  
Selection Of

Filaments are a type of wide-existing astronomical structure. It is a challenge to separate filaments from radio astronomical images, because their radiation is usually weak. What is more, filaments often mix with bright objects, e.g., stars, which makes it difficult to separate them. In order to extract filaments, A. Men’shchikov proposed a method “getfilaments” to find filaments automatically. However, the algorithm removed tiny structures by counting connected pixels number simply. Removing tiny structures based on local information might remove some part of the filaments because filaments in radio astronomical image are usually weak. In order to solve this problem, we applied morphology components analysis (MCA) to process each singe spatial scale image and proposed a filaments extraction algorithm based on MCA. MCA uses a dictionary whose elements can be wavelet translation function, curvelet translation function, or ridgelet translation function to decompose images. Different selection of elements in the dictionary can get different morphology components of the spatial scale image. By using MCA, we can get line structure, gauss sources, and other structures in spatial scale images and exclude the components that are not related to filaments. Experimental results showed that our proposed method based on MCA is effective in extracting filaments from real radio astronomical images, and images processed by our method have higher peak signal-to-noise ratio (PSNR).

scholarly journals Multiframe Astronomical Image Registration Based on Block Homography Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Ran Li ◽  
Lin Luo ◽  
Yu Zhang
Keyword(s):  
Image Registration ◽  
Signal To Noise Ratio ◽  
High Definition ◽  
Astronomical Image ◽  
Image Registration Algorithm ◽  
Homography Estimation ◽  
Noise Ratio ◽  
Astronomical Images ◽  
First Time

Due to the influence of atmospheric turbulence, a time-variate video of an observed object by using the astronomical telescope drifts randomly with the passing of time. Thereafter, a series of images is obtained snapshotting from the video. In this paper, a method is proposed to improve the quality of astronomical images only through multiframe image registration and superimposition for the first time. In order to overcome the influence of anisoplanatism, a specific image registration algorithm based on multiple local homography transformations is proposed. Superimposing registered images can achieve an image with high definition. As a result, signal-to-noise ratio, contrast-to-noise ratio, and definition are improved significantly.

Automated phase attribute-based picking applied to reflection seismics

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. V141-V150 ◽  
Author(s):  
Emanuele Forte ◽  
Matteo Dossi ◽  
Michele Pipan ◽  
Anna Del Ben
Keyword(s):  
Signal To Noise Ratio ◽  
Phase Method ◽  
Data Sets ◽  
Reflection Seismic ◽  
Instantaneous Phase ◽  
Whole Process ◽  
Specific Analysis ◽  
Reflection Seismics ◽  
Selection Of ◽  
Energy Package

We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user’s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques. Our picking procedure uses the cosine of the instantaneous phase to automatically detect and mark as a horizon any recorded event characterized by lateral phase continuity. A patching procedure, which exploits horizon parallelism, can be used to connect consecutive horizons marking the same event but separated by noise-related gaps. The picking process marks all coherent events regardless of their reflection strength; therefore, a large number of independent horizons can be constructed. To facilitate interpretation, horizons marking different phases of the same reflection can be automatically grouped together and specific horizons from each reflection can be selected using different possible methods. In the phase method, the algorithm reconstructs the reflected wavelets by averaging the cosine of the instantaneous phase along each horizon. The resulting wavelets are then locally analyzed and confronted through crosscorrelation, allowing the recognition and selection of specific reflection phases. In case the reflected wavelets cannot be recovered due to shape-altering processing or a low signal-to-noise ratio, the energy method uses the reflection strength to group together subparallel horizons within the same energy package and to select those satisfying either energy or arrival time criteria. These methods can be applied automatically to all the picked horizons or to horizons individually selected by the interpreter for specific analysis. We show examples of application to 2D reflection seismic data sets in complex geologic and stratigraphic conditions, critically reviewing the performance of the whole process.

scholarly journals EpiCore: Crowdsourcing Health Professionals to Verify Disease Outbreaks

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jennifer Olsen
Keyword(s):  
Health Professionals ◽  
Situational Awareness ◽  
Signal To Noise Ratio ◽  
Disease Outbreaks ◽  
Local Information ◽  
Outbreak Detection ◽  
False Information ◽  
The World ◽  
Geographic Regions ◽  
Human Animal

IntroductionEpiCore draws on the knowledge of a global community of human,animal, and environmental health professionals to verify informationon disease outbreaks in their geographic regions. By using innovativesurveillance techniques and crowdsourcing these experts, EpiCoreenables faster global outbreak detection, verification, and reporting.MethodsThrough a secure online platform, members are able to easily andquickly provide local information to expedite outbreak verification.EpiCore volunteer applications are vetted to ensure that they possessthe public health and epidemiologic expertise necessary to contributeto the platform.ResultsEpiCore currently has over 1600 members that span 135 countries.During the first 8 months of EpiCore’s launch, 172 requests forinformation to volunteers have been posted with an average responserate of over 80%.ConclusionsWith its geographical distribution of members and high responserate, EpiCore is poised to enable the world to verify potential outbreaksignals faster. By improving situational awareness, de-escalatingrumors or false information, and corroborating using other existingsources, EpiCore is able to reduce the signal to noise ratio in diseasesurveillance. Hence, by detecting and verifying outbreaks faster,health officials can generate early responses that can curb epidemicsand save lives.

scholarly journals DRPnet - Automated Particle Picking in Cryo-Electron Micrographs using Deep Regression

2019 ◽  
Author(s):  
Nguyen P. Nguyen ◽  
Jacob Gotberg ◽  
Ilker Ersoy ◽  
Filiz Bunyak ◽  
Tommi White
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electron Micrographs ◽  
Signal To Noise Ratio ◽  
Particle Analysis ◽  
User Interactions ◽  
Distance Map ◽  
Protein Particles ◽  
Selection Of ◽  
Specific Particle

AbstractSelection of individual protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based method to automatically detect particle centers from cryoEM micrographs. This is a challenging task because of the low signal-to-noise ratio of cryoEM micrographs and the size, shape, and grayscale-level variations in particles. We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined (or classified) to reduce false particle detections by the second CNN. This approach, entitled Deep Regression Picker Network or “DRPnet”, is simple but very effective in recognizing different grayscale patterns corresponding to 2D views of 3D particles. Our experiments showed that DRPnet’s first CNN pretrained with one dataset can be used to detect particles from a different datasets without retraining. The performance of this network can be further improved by re-training the network using specific particle datasets. The second network, a classification convolutional neural network, is used to refine detection results by identifying false detections. The proposed fully automated “deep regression” system, DRPnet, pretrained with TRPV1 (EMPIAR-10005) [1], and tested on β-galactosidase (EMPIAR-10017) [2] and β-galactosidase (EMPIAR-10061) [3], was then compared to RELION’s interactive particle picking. Preliminary experiments resulted in comparable or better particle picking performance with drastically reduced user interactions and improved processing time.

scholarly journals Collecting Microplastics in Gardens: Case Study (i) of Soil

2021 ◽  
Vol 9 ◽  
Author(s):  
Zahra Sobhani ◽  
Yunlong Luo ◽  
Christopher T. Gibson ◽  
Youhong Tang ◽  
Ravi Naidu ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Point Of View ◽  
Signal To Noise ◽  
Statistical Point ◽  
Image Mapping ◽  
Noise Ratio ◽  
In The Beginning ◽  
Raman Image ◽  
Selection Of

As an emerging contaminant, microplastic is receiving increasing attention. However, the contamination source is not fully known, and new sources are still being identified. Herewith, we report that microplastics can be found in our gardens, either due to the wrongdoing of leaving plastic bubble wraps to be mixed with mulches or due to the use of plastic landscape fabrics in the mulch bed. In the beginning, they were of large sizes, such as > 5 mm. However, after 7 years in the garden, owing to natural degradation, weathering, or abrasion, microplastics are released. We categorize the plastic fragments into different groups, 5 mm–0.75 mm, 0.75 mm–100 μm, and 100–0.8 μm, using filters such as kitchenware, meaning we can collect microplastics in our gardens by ourselves. We then characterized the plastics using Raman image mapping and a logic-based algorithm to increase the signal-to-noise ratio and the image certainty. This is because the signal-to-noise ratio from a single Raman spectrum, or even from an individual peak, is significantly less than that from a spectrum matrix of Raman mapping (such as 1 vs. 50 × 50) that contains 2,500 spectra, from the statistical point of view. From the 10 g soil we sampled, we could detect the microplastics, including large (5 mm–100 μm) fragments and small (<100 μm) ones, suggesting the degradation fate of plastics in the gardens. Overall, these results warn us that we must be careful when we do gardening, including selection of plastic items for gardens.

scholarly journals Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 129
Author(s):  
Mingdong Xu ◽  
Zhendong Yin ◽  
Yanlong Zhao ◽  
Zhilu Wu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Radio Spectrum ◽  
Local Information ◽  
Channel State ◽  
Single Node

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

Selection of Hyperspectral Endmember Extraction Algorithm for Tumor Delineation in Animal Models

2021 ◽  
Author(s):  
Deependra Mishra ◽  
John Wang ◽  
Steven T. Wang ◽  
Qian Cao ◽  
Helena Hurbon ◽  
...  
Keyword(s):  
Animal Models ◽  
Endmember Extraction ◽  
Tumor Delineation ◽  
Extraction Algorithm ◽  
Selection Of

Image Quality Improvement Using Shift Variant and Shift Invariant Based Wavelet Transform Methods

2017 ◽  
Vol 8 (3) ◽  
pp. 42-54
Author(s):  
Sugandha Agarwal ◽  
O. P. Singh ◽  
Deepak Nagaria ◽  
Anil Kumar Tiwari ◽  
Shikha Singh
Keyword(s):  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Computation Time ◽  
Performance Comparison ◽  
Local Information ◽  
Discrete Wavelet ◽  
Stationary Wavelet Transform ◽  
Visual Evaluation ◽  
Multi Scale ◽  
Image Quality Improvement

The concept of Multi-Scale Transform (MST) based image de-noising methods is incorporated in this paper. The shortcomings of Fourier transform based methods have been improved using multi-scale transform, which help in providing the local information of non-stationary image at different scales which is indispensable for de-noising. Multi-scale transform based image de-noising methods comprises of Discrete Wavelet Transform (DWT), and Stationary Wavelet Transform (SWT). Both DWT and SWT techniques are incorporated for the de-noising of standard images. Further, the performance comparison has been noted by using well defined metrics, such as, Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Computation Time (CT). The result shows that SWT technique gives better performance as compared to DWT based de-noising technique in terms of both analytical and visual evaluation.

scholarly journals A-PHOT: a new, versatile code for precision aperture photometry

2019 ◽  
Vol 622 ◽  
pp. A169 ◽  
Author(s):  
E. Merlin ◽  
S. Pilo ◽  
A. Fontana ◽  
M. Castellano ◽  
D. Paris ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Input Parameter ◽  
Repeated Measurements ◽  
Versatile Tool ◽  
Local Background ◽  
Resolution Imaging ◽  
Astronomical Images ◽  
Extragalactic Surveys ◽  
Photometric Measurements ◽  
Good Agreement

Aims. We present A-PHOT, a new publicly available code for performing aperture photometry on astronomical images, that is particularly well suited for multi-band extragalactic surveys. Methods.A-PHOT estimates the fluxes emitted by astronomical objects within a chosen set of circular or elliptical apertures. Unlike other widely used codes, it runs on predefined lists of detected sources, allowing for repeated measurements on the same list of objects on different images. This can be very useful when forced photometric measurement on a given position is needed. A-PHOT can also estimate morphological parameters and a local background flux, and compute on-the-fly individual optimized elliptical apertures, in which the signal-to-noise ratio is maximized. Results. We check the performance of A-PHOT on both synthetic and real test datasets: we explore a simulated case of a space-based high-resolution imaging dataset, investigating the input parameter space to optimize the accuracy of the performance, and we exploit the CANDELS GOODS-South data to compare the A-PHOT measurements with those from the survey legacy catalogs, finding good agreement overall. Conclusions.A-PHOT proves to a useful and versatile tool for quickly extracting robust and accurate photometric measurements and basic morphological information of galaxies and stars, with the advantage of allowing for various measurements of fluxes at any chosen position without the need of a full detection run, and for determining the basic morphological features of the sources.

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