scholarly journals A Generalized Additive Convolution Model for Efficient Deblurring of Camera Shaken Image

2015 ◽  
pp. 86-99
Author(s):  
Hong Deng ◽  
Dongwei Ren ◽  
Hongzhi Zhang ◽  
Kuanquan Wang ◽  
Wangmeng Zuo
Keyword(s):  
Convolution Model

scholarly journals Chinese Character Image Completion Using a Generative Latent Variable Model

2021 ◽  
Vol 11 (2) ◽  
pp. 624
Author(s):  
In-su Jo ◽  
Dong-bin Choi ◽  
Young B. Park
Keyword(s):  
Latent Variable ◽  
Chinese Character ◽  
Image Inpainting ◽  
Latent Variable Model ◽  
Chinese Characters ◽  
Image Completion ◽  
Variable Model ◽  
Variational Autoencoder ◽  
Convolution Model ◽  
Detection Technologies

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.

On the use of the Discrete-Time Convolution Model for the compensation of non-idealities within digital acquisition channels

Measurement ◽  
2007 ◽  
Vol 40 (5) ◽  
pp. 527-536 ◽  
Author(s):  
Pier Andrea Traverso ◽  
Gaetano Pasini ◽  
Antonio Raffo ◽  
Domenico Mirri ◽  
Gaetano Iuculano ◽  
...  
Keyword(s):  
Discrete Time ◽  
Convolution Model

Analysis of fMRI Data by Using Measures of Goodness of Fit to Convolution Model

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S81
Author(s):  
W Nakamura ◽  
Y Inouye
Keyword(s):  
Goodness Of Fit ◽  
Fmri Data ◽  
Convolution Model

scholarly journals The impact of air quality on its Baidu index: grey model analysis

2021 ◽  
Author(s):  
Leping Tu ◽  
Yan Chen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Grey Correlation ◽  
Convolution Model ◽  
Grey Correlation Analysis ◽  
And Control ◽  
The Impact ◽  
The Relationship

Abstract To investigate the relationship between air quality and its Baidu index, we collect the annual Baidu index of air pollution hazards, causes and responses. Grey correlation analysis, particle swarm optimization and grey multivariate convolution model are used to simulate and forecast the comprehensive air quality index. The result shows that the excessive growth of the comprehensive air quality index will lead to an increase in the corresponding Baidu index. The number of search for the causes of air quality has the closest link with the comprehensive air quality index. Strengthening the awareness of public about air pollution is conducive to the improvement of air quality. The result provides a reference for relevant departments to prevent and control air pollution.

Minimum entropy deconvolution and simplicity: A noniterative algorithm

Geophysics ◽  
1985 ◽  
Vol 50 (3) ◽  
pp. 394-413 ◽  
Author(s):  
Carlos A. Cabrelli
Keyword(s):  
Synthetic Data ◽  
Computational Method ◽  
Minimum Entropy ◽  
Autocorrelation Matrix ◽  
Numerical Tests ◽  
Convolution Model ◽  
Single Input ◽  
Definition Of ◽  
Single Matrix ◽  
New Criterion

Minimum entropy deconvolution (MED) is a technique developed by Wiggins (1978) with the purpose of separating the components of a signal, as the convolution model of a smooth wavelet with a series of impulses. The advantage of this method, as compared with traditional methods, is that it obviates strong hypotheses over the components, which require only the simplicity of the output. The degree of simplicity is measured with the Varimax norm for factor analysis. An iterative algorithm for computation of the filter is derived from this norm, having as an outstanding characteristic its stability in presence of noise. Geometrical analysis of the Varimax norm suggests the definition of a new criterion for simplicity: the D norm. In case of multiple inputs, the D norm is obtained through modification of the kurtosis norm. One of the most outstanding characteristics of the new criterion, by comparison with the Varimax norm, is that a noniterative algorithm for computation of the deconvolution filter can be derived from the D norm. This is significant because the standard MED algorithm frequently requires in each iteration the inversion of an autocorrelation matrix whose order is the length of the filter, while the new algorithm derived from the D norm requires the inversion of a single matrix. On the other hand, results of numerical tests, performed jointly with Graciela A. Canziani, show that the new algorithm produces outputs of greater simplicity than those produced by the traditional MED algorithm. These considerations imply that the D criterion yields a new computational method for minimum entropy deconvolution. A section of numerical examples is included, where the results of an extensive simulation study with synthetic data are analyzed. The numerical computations show in all cases a remarkable improvement resulting from use of the D norm. The properties of stability in the presence of noise are preserved as shown in the examples. In the case of a single input, the relation between the D norm and the spiking filter is analyzed (Appendix B).

Simultaneous inversion of Q and reflectivity using dictionary learning

Geophysics ◽  
2021 ◽  
pp. 1-69
Author(s):  
Jie Shao ◽  
Yibo Wang
Keyword(s):  
Conventional Method ◽  
Dictionary Learning ◽  
Computational Efficiency ◽  
Synthetic Data ◽  
Inversion Method ◽  
Learning Method ◽  
Q Value ◽  
Value Set ◽  
Simultaneous Inversion ◽  
Convolution Model

Quality factor ( Q) and reflectivity are two important subsurface properties in seismic data processing and interpretation. They can be calculated simultaneously from a seismic trace corresponding to an anelastic layered model by a simultaneous inversion method based on the nonstationary convolution model. However, the conventional simultaneous inversion method calculates the optimum Q and reflectivity based on the minimum of the reflectivity sparsity by sweeping each Q value within a predefined range. As a result, the accuracy and computational efficiency of the conventional method depend heavily on the predefined Q value set. To improve the performance of the conventional simultaneous inversion method, we have developed a dictionary learning-based simultaneous inversion of Q and reflectivity. The parametric dictionary learning method is used to update the initial predefined Q value set automatically. The optimum Q and reflectivity are calculated from the updated Q value set based on minimizing not only the sparsity of the reflectivity but also the data residual. Synthetic data and two field data sets were used to test the effectiveness of our method. The results demonstrated that our method can effectively improve the accuracy of these two parameters compared to the conventional simultaneous inversion method. In addition, the dictionary learning method can improve computational efficiency up to approximately seven times when compared to the conventional method with a large predefined dictionary.

Seismic-to-well tie of a field of the Nigerian Delta

2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed
Keyword(s):  
Seismic Data ◽  
Processing Parameters ◽  
Seismic Inversion ◽  
Seismic Wavelet ◽  
Statistical Measures ◽  
Convolution Model ◽  
Data Driven Approach ◽  
Well Data ◽  
Seismic Reflectors ◽  
Matching Techniques

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,

Analytical Convolution Model of Cutting Forces in 3-D Ball End Milling

2001 ◽  
Author(s):  
Richard Y. Chiou ◽  
Bing Zhao
Keyword(s):  
Cutting Force ◽  
Frequency Domain ◽  
Cutting Forces ◽  
End Milling ◽  
Force Model ◽  
Three Dimensional Model ◽  
Calculation Algorithm ◽  
Ball End Milling ◽  
Convolution Model ◽  
Force Calculation

Abstract This paper presents an analytical convolution model of dynamic cutting forces in ball end milling of 3-D plane surfaces. The model takes into account the instantaneous slope on a sculptured surface to establish the chip geometry in cutting force calculation algorithm. A three-dimensional model of cutting forces in ball end milling is presented in terms of material properties, cutting parameters, machining configuration, and tool/work geometry. Based on the relationship of the local cutting force, chip load and engaged boundary, the total cutting force model is established via the angle domain convolution integration of the local forces in the feed, cross feed, axial direction, and inclination angle. The convolution integral leads to a periodic function of cutting forces in the angle domain and an explicit expression of the dynamic cutting force components in the frequency domain. Following the theoretical analysis, experimental study is discussed to illustrate the implementation procedure for force identification, and frequency domain data are presented to verify the analytical results.

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