An Adaptive Filtering Algorithm Based on Improved Gray Relational Analysis

2011 ◽  
Vol 148-149 ◽  
pp. 483-486
Author(s):  
Chun Yan Huang ◽  
Yan Ling Li
Keyword(s):  
Adaptive Filtering ◽  
Noise Removal ◽  
Image Features ◽  
Gray Relational Analysis ◽  
Filtering Algorithm ◽  
Relational Analysis ◽  
Mean Filter ◽  
Gray Relation Analysis ◽  
Mixed Noise ◽  
Mixed Noise Removal

Because of the characteristic of the gray relation analysis and the advantage of the alpha-trimmed mean filter, an efficient technique for mixed noise removal in images was proposed. This algorithm can adjust the filter coefficients adaptively according to various pieces of the image features. Experiment results show that the proposed algorithm which greatly improved efficiently, it not only can remove mixed noise in image, but also can keep the details of the image.

scholarly journals Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Hongjin Ma ◽  
Yufeng Nie
Keyword(s):  
Diffusion Model ◽  
Anisotropic Diffusion ◽  
Impulse Noise ◽  
Noise Removal ◽  
Image Features ◽  
Weighted Mean ◽  
Optimal Direction ◽  
Mean Filter ◽  
Mixed Noise ◽  
Mixed Noise Removal

A mixed noise removal algorithm combining adaptive directional weighted mean filter and improved adaptive anisotropic diffusion model is proposed. Firstly, a noise classification method is introduced to divide all pixels into two types as the pixels corrupted by impulse noise and the pixels corrupted by Gaussian noise. Then an adaptive directional weighted mean filter is developed to remove impulse noise, which can adaptively select the optimal direction template from twelve direction templates and replace the gray level of each impulse noise corrupted pixel by the weighted mean gray level of pixels on the optimal direction template. Finally, an improved adaptive anisotropic diffusion model is developed to remove Gaussian noise in the initial denoised image, which can finely classify image features as smooth regions, edges, corners, and isolated noises by characteristic parameters and variance parameter and conduct adaptive diffusion for different image features by designing reasonable eigenvalues of diffusion tensor. A large number of experimental results show that the proposed algorithm outperforms many existing main mixed noise removal methods in terms of image denoising and detail preservation.

Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation

2017 ◽  
Vol 26 (7) ◽  
pp. 3171-3186 ◽  
Author(s):  
Tao Huang ◽  
Weisheng Dong ◽  
Xuemei Xie ◽  
Guangming Shi ◽  
Xiang Bai
Keyword(s):  
Noise Removal ◽  
Mixture Modeling ◽  
Low Rank ◽  
Scale Mixture ◽  
Low Rank Approximation ◽  
Mixed Noise ◽  
Rank Approximation ◽  
Mixed Noise Removal

scholarly journals Application of Gray Relation Analysis for Multi-Response Optimization of Plasma Heat Assisted Turning Performance

2018 ◽  
Vol 7 (2.32) ◽  
pp. 143
Author(s):  
Thella Babu Rao ◽  
Venu Pilli ◽  
Nallamotu Revanth Sai Venkat ◽  
Nagandla Pavan ◽  
Thalari Shiva Ram
Keyword(s):  
Surface Roughness ◽  
Plasma Heating ◽  
Cutting Speed ◽  
Gray Relational Analysis ◽  
Machining Performance ◽  
Relational Analysis ◽  
Gray Relation Analysis ◽  
Cutting Length ◽  
Response Optimization ◽  
Hardened En24

This paper presents optimization of plasma heat assisted turning process for machining hardened EN24 die steel (53HRC) by using gray relational analysis. Flank wear and surface roughness (Ra) are experimentally measured as the process performance characteristics under varying conditions of preheating temperature, cutting speed and cutting length. The plasma heating approach was implemented to preheat the workpiece. The machining experiments were conducted according to the L16 design of experiments. Since the chosen machining performance indicators are found with confliction for the chosen process variables, the problem is treated as multi-response optimization problem to minimize the tool wear and surface roughness simultaneously. Therefore, the problem was solved by implementing the gray relational analysis and the derived optimal machining conditions were analysed and reported.  

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