scholarly journals Noise-Transfer2Clean: Denoising cryo-EM images based on noise modeling and transfer

2021 ◽  
Author(s):  
Hongjia Li ◽  
Hui Zhang ◽  
Xiaohua Wan ◽  
Zhidong Yang ◽  
Chengmin Li ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Main Idea ◽  
Noise Model ◽  
Statistical Characteristics ◽  
Natural Image ◽  
Macromolecular Complex ◽  
Signal Restoration ◽  
Denoising Method

Motivation: Cryo-electron microscopy (cryo-EM) is a widely-used technology for ultrastructure determination, which constructs the three-dimensional (3D) structures of protein and macromolecular complex from a set of two-dimensional (2D) micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. Results: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly discovering the noise model of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise's true properties. To our knowledge, NT2C is the first denoising method that resolves the complex noise model in cryo-EM images. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising and specimen signal restoration, comparing with the state-of-art methods. A real-world case study shows that NT2C can improve the recognition rate on hard-to-identify particles by 19% in the particle picking task.

Conformational characterization of nucleosome structure by Electron Microscopy

1993 ◽  
Vol 51 ◽  
pp. 194-195
Author(s):  
Gregory J. Czarnota
Keyword(s):  
Electron Microscopy ◽  
Structural Changes ◽  
Physiological State ◽  
Gene Activation ◽  
Three Dimensional ◽  
Macromolecular Complex ◽  
Ultrastructural Studies ◽  
Ionic Environment ◽  
Nucleosome Structure ◽  
Dimensional Reconstruction

Chromatin structure at the fundamental level of the nucleosome is important in vital cellular processes. Recent biochemical and genetic analyses show that nucleosome structure and structural changes are very active participants in gene expression, facilitating or inhibiting transcription and reflecting the physiological state of the cell. Structural states and transitions for this macromolecular complex, composed of DNA wound about a heterotypic octamer of variously modified histone proteins, have been measured by physico-chemical techniques and by enzyme-accessibility and are recognized to occur with various post-translational modifications, gene activation, transformation and with ionic-environment. In spite of studies which indicate various forms of nucleosome structure, all current x-ray and neutron diffraction studies have consistently resulted in only one structure, suggestive of a static conformation. In contrast, two-dimensional electron microscopy studies and three-dimensional reconstruction techniques have yielded different structures. These fundamental differences between EM and other ultrastructural studies have created a long standing quandary, which I have addressed and resolved using spectroscopic electron microscopy and statistical analyses of nucleosome images in a study of nucleosome structure with ionic environment.

scholarly journals A Field Measurement Based Wind Characteristics Analysis of a Typhoon in Near-Ground Boundary Layer

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 873
Author(s):  
Dandan Xia ◽  
Liming Dai ◽  
Li Lin ◽  
Huaifeng Wang ◽  
Haitao Hu
Keyword(s):  
Turbulence Intensity ◽  
Field Measurement ◽  
Three Dimensional ◽  
Wind Data ◽  
Statistical Characteristics ◽  
Gust Factor ◽  
Characteristics Analysis ◽  
Wind Characteristics ◽  
The Mean ◽  
The Relationship

The field measurement was conducted to observe the wind field data of West Pacific typhoon “Maria” in this research. With the application of ultrasonic anemometers installed in different heights (10 m, 80 m, 100 m) of the tower, the three dimensional wind speed data of typhoon “Maria” was acquired. In addition, vane-type anemometers were installed to validate the accuracy of the wind data from ultrasonic anemometers. Wind characteristics such as the mean wind profile, turbulence intensity, integral length scale, and wind spectrum are studied in detail using the collected wind data. The relationship between the gust factor and turbulence intensity was also studied and compared with the existing literature to demonstrate the characteristics of Maria. The statistical characteristics of the turbulence intensity and gust factor are presented. The corresponding conclusion remarks are expected to provide a useful reference for designing wind-resistant buildings and structures.

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

2015 ◽  
Vol 14 (02) ◽  
pp. 1550017
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Classical Problem ◽  
Statistical Parameter ◽  
Noise Model ◽  
Natural Image ◽  
Bayesian Techniques ◽  
Generalized Gamma ◽  
Gamma Density

The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

scholarly journals The new agroforestry paradigm’s formation

2019 ◽  
Vol 89 (10) ◽  
pp. 1044-1051
Author(s):  
Aleksandr S. Rulev ◽  
Anna M. Pugacheva
Keyword(s):  
Three Dimensional ◽  
Main Idea ◽  
Agroforestry Systems ◽  
Agricultural Landscapes ◽  
Dust Storms ◽  
Erosion Processes ◽  
Short Period ◽  
International Projects ◽  
Forest Belts ◽  
Protective Forest

From acceptance of the 1948 Plan of Field-Protective Afforestation to the present (2019), this article considers the new agroforestry paradigms protracted formation. Scientific achievements from the 1940s, introduced into practice, served as the basis for decisions on natures global transformation. Pilot facilities from the beginning of the 20th century (the Bogdinsky agroforestry stronghold, the Stone-steppe oasis) still serve as reference objects for agroforest reclamation of territories, with a scientific approach that allows them to function productively today. The plans main idea is to combat drought and desertification of steppe lands, erosion processes, and to prevent sand and dust storms. Creation of 5709 thousand hectares of protective forests, afforestation of 1106 thousand hectares of ravines, fixing and afforestation of sand on an area of 322 thousand hectares, and implementation of many planned activities during a short period locate this plan among other ambitious international projects. The authors draw attention to the time of creation and the volume of plantings of paramount importance, that is, state protective forest belts and protective forest plantations. Understanding the importance of agroforestry for modern agricultural landscapes led to formation of sustainable and durable agroforestry systems in subarid landscapes based on a combination of agricultural and landscape-ecological ideologies. Allegedly, considering terrain ecotopes, three-dimensional evaluation of the agrolandscape and a non-linear approach make it possible to create multifunctional, highly productive agroforestry systems in critical agriculture zones.

scholarly journals 3D Robot Vision System through 2D Shape Based Matching Using Gaussian Smoothing for Gluing Application

2018 ◽  
Vol 7 (4.33) ◽  
pp. 487
Author(s):  
Mohamad Haniff Harun ◽  
Mohd Shahrieel Mohd Aras ◽  
Mohd Firdaus Mohd Ab Halim ◽  
Khalil Azha Mohd Annuar ◽  
Arman Hadi Azahar ◽  
...  
Keyword(s):  
Template Matching ◽  
Shape Matching ◽  
Vision System ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Robot Vision ◽  
Region Of Interest ◽  
Gaussian Smoothing ◽  
Detection Approach ◽  
Vision Algorithm

This investigation is solely on the adaptation of a vision system algorithm to classify the processes to regulate the decision making related to the tasks and defect’s recognition. These idea stresses on the new method on vision algorithm which is focusing on the shape matching properties to classify defects occur on the product. The problem faced before that the system required to process broad data acquired from the object caused the time and efficiency slightly decrease. The propose defect detection approach combine with Region of Interest, Gaussian smoothing, Correlation and Template Matching are introduced. This application provides high computational savings and results in better recognition rate about 95.14%. The defects occur provides with information of the height which corresponds by the z-coordinate, length which corresponds by the y-coordinate and width which corresponds by the x-coordinate. This data gathered from the proposed system using dual camera for executing the three dimensional transformation.  

scholarly journals Pulmonary Lesion Subtypes Recognition of COVID-19 From Radiomics Data With Three Dimensional Texture Characterization in CT Images

2021 ◽  
Author(s):  
Wei Li ◽  
Yangyong Cao ◽  
Kun Yu ◽  
Yibo Cai ◽  
Feng Huang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Critical Role ◽  
Three Dimensional ◽  
Treatment Decision ◽  
Ground Glass Opacity ◽  
Ct Images ◽  
Classification Model ◽  
Volume Data ◽  
Selection Algorithm ◽  
Adaptive Selection

Abstract Background: The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. Method: The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are ground-glass opacity (GGO), cord, solid and subsolid. A computer aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three dimensional texture descriptors are applied on the volume data of lesions as well as shape and First order features. The massive feature data are selected by hybrid adaptive selection algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. Results: There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (0.9306, 0.9684, 0.9958, and 0.9430), the recall is (0.9552, 0.9158, 0.9580 and 0.8075) and the f-score is (0.93.84, 0.92.37, 0.95.47, and 84.42). Conclusion: The method is evaluated in multiple sufficient experiments. The results show that the 3D radiomics features chosen by hybrid adaptive selection algorithm can better express the advanced information of the lesion data. The classification model obtains a good performance and is compared the models of COVID-19 in the stat of art, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

A method of locating the 3D centers of retroreflectors based on deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
BinBin Zhang ◽  
Fumin Zhang ◽  
Xinghua Qu
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Measurement Techniques ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Large Field ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Ellipse Detection

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

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