A Non-Singleton Interval Type-2 Fuzzy Logic System for universal image noise removal using Quantum-behaved Particle Swarm Optimization

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

doi:10.1109/fuzzy.2011.6007505

Cited by 13 publications

(6 citation statements)

References 73 publications

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“…To use the advantage of the fuzzy rule, valuable studies have been conducted. For example, Zhai [36] proposed a non-singleton interval type-2 fuzzy logic system for mixed Gaussian and impulse noise removal, and quantitatively and visually obtained excellent results because the system was designed based on QPSO. In the current study, OLB-QPSO is proposed based on the communication effects study in the mass communication field.…”

Section: Olb-qpsomentioning

confidence: 97%

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

et al. 2015

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Section: Olb-qpsomentioning

confidence: 97%

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

et al. 2015

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“…Since IT2 FSs include an extra degree of freedom, studies have shown that FLSs which utilise IT2 FSs (other parameters such as the number of rules remaining constant) can outperform their T1 FLSs counterparts in a variety of applications [7]- [13].…”

Section: B Interval Type-2 Fuzzy Setsmentioning

confidence: 99%

“…Due to this extra degree of freedom, generally, T2 FSs provide a better ability to capture variation in uncertainty levels which is omnipresent in the real-world. [7]- [13].…”

Section: Introductionmentioning

confidence: 99%

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels

Pekaslan

Wagner

Garibaldi

2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input Fuzzy Sets (FSs), Type-2 (T2) input FSs provide the ability to also capture variation in uncertainty levels by means of their extra degrees of freedom. Specifically, in this paper, we propose a strategy to design Interval Type-2 (IT2) input Membership Functions (MFs) in an online manner to ensure the parameters of input MFs are updated dynamically, thus capturing varying levels of uncertainty affecting systems' inputs. In this strategy, first, uncertainty detection is performed over a given time-frame (the Uncertainty Estimation Time-frame) and Type-1 (T1) input MFs are constructed by utilising the detected uncertainty level. Second, the variation of the uncertainty levels over a sliding window (the Uncertainty Variation Window) is used to capture the degree of variation in the detected uncertainty levels over time, which in turn informs the size of the Footprint of Uncertainty (FOU) of the IT2 MF associated with the T1 principal MF. Using time-series prediction experiments as an initial evaluation and demonstration platform for the proposed architecture, we show that the proposed strategy of designing IT2 input MFs has the potential to deliver performance benefits. Specifically, it allows systems to not only adapt to specific uncertainty levels but also to be more resilient to the variation of said uncertainty levels over time, thus offering a pathway to robust performance in real-world applications.

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“…CIE lab color spaces are used to evaluate the measures for proving the closeness of the uncorrupted original image to the filtered image. Above mentioned measures or their combination is used in the literature to evaluate the results of the most filters [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Three parameters namely MSE, PSNR and CoC are used for the evaluation of the results in the current study.…”

Section: Parameters For Evaluationmentioning

confidence: 99%

Performance Evaluation and Comparative Analysis of Different Filters for Noise Reduction

Kaur¹,

Maini²

2016

IJIGSP

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Abstract-The quality of microscopic images is generally degraded during the image acquisition by quantizing noise, electrical noise, light illumination etc. Noise reduction is considered as a very important preprocessing step as the quality of the images can determine the accuracy of the results. The work done focuses on the noise reduction using different filters on the different types of noises applied on the common digital images and specifically the Leukemia images. 40 images were taken for the comparison purpose; 20 digital images and 20 Leukemia images of different types of Leukemia. The qualitative as well as quantitative analysis of the performance of the filters on the different noises is done. For the quantitative analysis the parameters used for the evaluation of the images are MSE, PSNR and CoC. For the qualitative analysis visual analysis in terms of quality is also done using the resultant images and their histograms. Simulation has been done in Matlab 11b. From the test cases it has been observed that Adaptive Filter produces good results on Salt and Pepper, Speckle and Gaussian noise in case of the digital images. Whereas in case of Leukemia images results of Median Filter are best for the Gaussian, Poisson and Speckle noise corrupted images.

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A Non-Singleton Interval Type-2 Fuzzy Logic System for universal image noise removal using Quantum-behaved Particle Swarm Optimization

Cited by 13 publications

References 73 publications

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels

Performance Evaluation and Comparative Analysis of Different Filters for Noise Reduction

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