A side scan sonar image denoising algorithm based on compound of fuzzy weighted average and Kalman filter

2012 ◽  
Author(s):  
Xiufen Ye ◽  
Peng Li ◽  
Yingying Deng
Keyword(s):  
Kalman Filter ◽  
Image Denoising ◽  
Weighted Average ◽  
Side Scan Sonar ◽  
Sonar Image ◽  
Fuzzy Weighted Average

Sonar image denoising method based on NSCT circle sample

2009 ◽  
Vol 29 (1) ◽  
pp. 68-70
Author(s):  
Chun-rui TANG ◽  
Dan-dan LIU
Keyword(s):  
Image Denoising ◽  
Denoising Method ◽  
Sonar Image

scholarly journals Induced intuitionistic fuzzy ordered weighted averaging: Weighted average operator and its application to business decision-making

2014 ◽  
Vol 11 (2) ◽  
pp. 839-857 ◽  
Author(s):  
Zeng Shouzhen ◽  
Wang Qifeng ◽  
José Merigó ◽  
Pan Tiejun
Keyword(s):  
Decision Making ◽  
Weighted Average ◽  
Aggregation Operator ◽  
Weighted Averaging ◽  
Business Decision ◽  
Ordered Weighted Averaging ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Weighted Average ◽  
Decision Making Problem ◽  
Selection Of

We present the induced intuitionistic fuzzy ordered weighted averaging-weighted average (I-IFOWAWA) operator. It is a new aggregation operator that uses the intuitionistic fuzzy weighted average (IFWA) and the induced intuitionistic fuzzy ordered weighted averaging (I-IFOWA) operator in the same formulation. We study some of its main properties and we have seen that it has a lot of particular cases such as the IFWA and the intuitionistic fuzzy ordered weighted averaging (IFOWA) operator. We also study its applicability in a decision-making problem concerning strategic selection of investments. We see that depending on the particular type of I-IFOWAWA operator used, the results may lead to different decisions.

Fuzzy weighted average: An improved algorithm

1992 ◽  
Vol 49 (3) ◽  
pp. 307-315 ◽  
Author(s):  
Tian-Shy Liou ◽  
Mao-Jiun J. Wang
Keyword(s):  
Weighted Average ◽  
Fuzzy Weighted Average ◽  
Improved Algorithm

Application of Kalman Filtering for Natural Gray Image Denoising

2011 ◽  
Vol 187 ◽  
pp. 92-96 ◽  
Author(s):  
Zhi Kai Huang ◽  
De Hui Liu ◽  
Xing Wang Zhang ◽  
Ling Ying Hou
Keyword(s):  
Kalman Filter ◽  
Image Denoising ◽  
Kalman Filtering ◽  
Moving Average ◽  
Arma Model ◽  
Adaptive Method ◽  
Natural Image ◽  
Autoregressive Moving Average ◽  
Processing Step ◽  
Reduction Methods

Image denoising is one of the classical problems in digital image processing, and has been studied for nearly half a century due to its important role as a pre-processing step in various image applications. In this work, a denoising algorithm based on Kalman filtering was used to improve natural image quality. We have studied noise reduction methods using a hybrid Kalman filter with an autoregressive moving average (ARMA) model that the coefficients of the AR models for the Kalman filter are calculated by solving for the minimum square error solutions of over-determined linear systems. Experimental results show that as an adaptive method, the algorithm reduces the noise while retaining the image details much better than conventional algorithms.

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