Universal Image Noise Removal Filter Based on Type-2 Fuzzy Logic System and Qpso

Zhai, Daoyuan; Hao, Minshen; Mendel, Jerry M.

doi:10.1142/s0218488512400211

Cited by 16 publications

(11 citation statements)

References 65 publications

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“…In [8], the authors present an impulse detector based on three input and one output first-order Sugeno-type IT2 FLS, while in [34] a non-singleton IT2 FLS was proposed. In [35], a filter based on T2 FLS techniques was presented, and in [36] a filter based on an adaptive T2 fuzzy median was proposed.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, a selective feedback fuzzy neural network based on IT2 FLS was introduced in [35]. In the research papers [8,[34][35][36][37], filtering accuracy is validated using visual appreciation, mean squared error (MSE), mean absolute error (MAE), the peak signal to noise ratio (PSNR), and normalized mean square error (NMSE).…”

Section: Discussionmentioning

confidence: 99%

“…In [35], validation was performed with MSE, while in [36], MAE and PSNR were used. In [34,37], images were corrupted with Gaussian noise ranging from 30% to 70%, 0.01% and 0.06% respectively. Additionally, [34] also was corrupted with 30% of impulse noise and was validated using visual appreciation and MSE, while in [37] MAE and NMSE was used.…”

Section: Discussionmentioning

confidence: 99%

“…In Zhai Daoyuan, et al (2012) [34] an approach for removing mixed Gaussian and impulse noise removal from images based on a non-singleton IT2 FLS was proposed; this also promotes the use of a quantum-behaved Particle Swarm Optimization (PSO) algorithm for determining the parameters of the FLS. This noise is considered to be one of the most essential topics in the domain of image restoration, and this approach was tested in black and white benchmark images corrupted with impulse noise.…”

Section: T2 Fs In Image Filteringmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: T2 Fs In Image Filteringmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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“…We use piecewise linear MF models and Quantum Particle Swarm Optimization (QPSO) (e.g., [46]- [51]) as our MF parameter optimization method. Any other swarm optimization procedure can be used instead of QPSO.…”

Section: A1 Optimizing the Regression Coefficientsmentioning

confidence: 99%

Non-linear Variable Structure Regression (VSR) and its application in time-series forecasting

Korjani

Mendel

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Variable Structure Regression (VSR) is a new kind of non-linear regression model, which simultaneously determines the exact mathematical structure of non-linear regressors and how many regressors there are, thereby freeing the end user from trial and error time-consuming studies to determine these. The results are based on an iterative procedure for optimizing parameters and automatically identifying the structure of the VSR model. A novel feature of this new model is it not only uses a linguistic term for a variable but it also uses the complement of that term. It also provides the end user with a physical understanding of the regressors. A Monte Carlo study shows the practical accuracy of VSR model on the classical Gas Furnace time-series prediction problem. VSR ranked #1 compared to five other methods.

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Forecasting by TSK general type‐2 fuzzy logic systems optimized with genetic algorithms

Chen

Wang

2017

Optim Control Appl Methods

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SummaryResearching the theory and applications of general type-2 fuzzy logic systems (GT2 FLSs) has become a hot orientation in recent years. The permanent-magnetic drive (PMD) affected by uncertainties is an emerging technology. This paper designs a type of Takagi-Sugeno-Kang GT2 FLSs to investigate PMD temperature forecasting problems. Genetic algorithms are used to optimize the parameters of Takagi-Sugeno-Kang GT2 FLSs, according to the asymptotic way. The primary membership functions (MFs) of the antecedent and input measurements of the proposed systems are chosen as the Gaussian-type MFs with uncertain standard deviations, and the consequent parameters are selected as crisp numbers, whereas the secondary MFs (vertical slices) are selected as a triangle type. Noisy data of PMD temperature are used for training and testing the proposed T2 FLS forecasting methods. Numerical simulation studies and convergence analysis illustrate that the proposed GT2 FLSs outperform their type-1 and interval type-2 FLSs.

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Universal Image Noise Removal Filter Based on Type-2 Fuzzy Logic System and Qpso

Cited by 16 publications

References 65 publications

Review of Recent Type-2 Fuzzy Image Processing Applications

Review of Recent Type-2 Fuzzy Image Processing Applications

Non-linear Variable Structure Regression (VSR) and its application in time-series forecasting

Forecasting by TSK general type‐2 fuzzy logic systems optimized with genetic algorithms

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