New second order Mumford–Shah model based on Γ-convergence approximation for image processing

2016 ◽  
Vol 76 ◽  
pp. 641-647 ◽  
Author(s):  
Jinming Duan ◽  
Wenqi Lu ◽  
Zhenkuan Pan ◽  
Li Bai
Keyword(s):  
Image Processing ◽  
Second Order ◽  
Model Based ◽  
Γ Convergence

scholarly journals PROPOSAL OF THE SPATIAL DEPENDENCE EVALUATION FROM THE POWER SEMIVARIOGRAM MODEL

2017 ◽  
Vol 23 (3) ◽  
pp. 461-475 ◽  
Author(s):  
Ismael Canabarro Barbosa ◽  
Edemar Appel Neto ◽  
Enio Júnior Seidel ◽  
Marcelo Silva de Oliveira
Keyword(s):  
Spatial Correlation ◽  
Spatial Dependence ◽  
Second Order ◽  
Power Model ◽  
Semivariogram Model ◽  
Model Based ◽  
Symmetrical Distribution ◽  
Model Adjustment ◽  
Equivalent Factor

Abstract: In Geostatistics, the use of measurement to describe the spatial dependence of the attribute is of great importance, but only some models (which have second-order stationarity) are considered with such measurement. Thus, this paper aims to propose measurements to assess the degree of spatial dependence in power model adjustment phenomena. From a premise that considers the equivalent sill as the estimated semivariance value that matches the point where the adjusted power model curves intersect, it is possible to build two indexes to evaluate such dependence. The first one, SPD * , is obtained from the relation between the equivalent contribution (α) and the equivalent sill (C * = C 0 + α), and varies from 0 to 100% (based on the calculation of spatial dependence areas). The second one, SDI * , beyond the previous relation, considers the equivalent factor of model (FM * ), which depends on the exponent β that describes the force of spatial dependence in the power model (based on spatial correlation areas). The SDI * ,for β close to 2, assumes its larger scale, varying from 0 to 66.67%. Both indexes have symmetrical distribution, and allow the classification of spatial dependence in weak, moderate and strong.

scholarly journals Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyu Tong ◽  
Kailiang Shao ◽  
Xilin Luo ◽  
Huiming Duan
Keyword(s):  
Image Processing ◽  
Prediction Model ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Middle Point ◽  
Filtering Algorithm ◽  
Grey Correlation ◽  
Grey Prediction Model ◽  
Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

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