A Fuzzy Logic Approach for Image Restoration and Content Preserving

Image filtering, which removes or reduces noises from the contaminated images, is an important task in image processing. This paper presents a novel approach to the problem of noise reduction for gray-scale images. The proposed technique is able to remove the noise component, while adapting itself to the local noise intensity. In this way, the proposed algorithm can be considered as a modification of the median filter driven by fuzzy membership functions. Experimental results are compared to static median filter by numerical measures and visual inspection. As was expected, the new filter shows better performances.

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New filtering technique for the impulsive noise reduction in color images

Mathematical Problems in Engineering ◽

10.1155/s1024123x04110016 ◽

2004 ◽

Vol 2004 (1) ◽

pp. 79-91 ◽

Cited By ~ 12

Author(s):

B. Smolka ◽

A. Chydzinski ◽

K. N. Plataniotis ◽

A. N. Venetsanopoulos

Keyword(s):

Noise Reduction ◽

Impulsive Noise ◽

Median Filter ◽

Computationally Efficient ◽

Noise Component ◽

Fuzzy Membership Functions ◽

Novel Approach ◽

Filtering Technique ◽

Vector Median ◽

Impulsive Noise Reduction

We present a novel approach to the problem of impulsive noise reduction for colorimages. The new image-filtering technique is based on the maximization of the similarities between pixels in the filtering window. Themethod is able to remove the noise component, while adapting itself to the local image structure. In this way, the proposed algorithm eliminates impulsive noise while preserving edges and fine image details. Since the algorithm can be considered as a modification of the vector median filter driven by fuzzy membership functions, it is fast, computationally efficient, and easy to implement. Experimental results indicate that the new method is superior, in terms of performance, to algorithms commonly used for impulsive noise reduction.

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A novel approach to the derivation of fuzzy membership functions using the Falcon-MART architecture

Pattern Recognition Letters ◽

10.1016/s0167-8655(01)00033-2 ◽

2001 ◽

Vol 22 (9) ◽

pp. 941-958 ◽

Cited By ~ 36

Author(s):

C Quek ◽

W.L Tung

Keyword(s):

Fuzzy Membership ◽

Membership Functions ◽

Fuzzy Membership Functions ◽

Novel Approach

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Finding Topics in Email Using Formal Concept Analysis and Fuzzy Membership Functions

Advances in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-540-68825-9_11 ◽

2008 ◽

pp. 108-113 ◽

Cited By ~ 1

Author(s):

Liqiang Geng ◽

Larry Korba ◽

Yunli Wang ◽

Xin Wang ◽

Yonghua You

Keyword(s):

Formal Concept Analysis ◽

Concept Analysis ◽

Fuzzy Membership ◽

Formal Concept ◽

Membership Functions ◽

Fuzzy Membership Functions

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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Journal of the Operations Research Society of China ◽

10.1007/s40305-021-00354-9 ◽

2021 ◽

Author(s):

Jia-Bin Zhou ◽

Yan-Qin Bai ◽

Yan-Ru Guo ◽

Hai-Xiang Lin

Keyword(s):

Support Vector Machine ◽

Negative Impact ◽

Twin Support Vector Machine ◽

Fuzzy Membership ◽

Support Vector ◽

Membership Functions ◽

Fuzzy Membership Functions ◽

Intuitionistic Fuzzy ◽

Benchmark Datasets ◽

The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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