Fuzzy Image Enhancement Based on Algebraic Function and Cycloid Arc Length

Yang, Libao; Zenian, Suzelawati; Zakaria, Rozaimi

doi:10.1109/iicaiet51634.2021.9573686

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Fuzzy sets can deal with some uncertain factors better than classical mathematics. Fuzzy technology is also increasingly used for image processing [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Yang¹,

Zenian²,

Zakaria³

2022

IJACSA

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Image enhancement is an important method in the process of image processing. This paper proposes an image enhancement method base on an improved fuzzy c-means clustering. The method consists of the following steps: firstly, proposed a fuzzy c-means clustering with a cooperation center(FCM-co). Secondly, using the FCM-co, divide the image pixels into different clusters and marked membership values to those clusters. Thirdly, modify the membership values. Finally, calculate the new pixel gray levels. This enhancement method can overcome the disadvantage of overexposure and better retain image details. Through the experiment, the test results show that the proposed enhancement method could achieve better performance.

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“…Fuzzy sets can deal with some uncertain factors better than classical mathematics. Fuzzy technology is also increasingly used for image processing [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Yang¹,

Zenian²,

Zakaria³

2022

IJACSA

Self Cite

View full text Add to dashboard Cite

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“…Dawayet et al [25] proposed a fuzzification and a defuzzification method. Yang et al [26] proposed an INT operator based on cycloid arc length function. Fuzzy C-means clustering is also a good method for image enhancement [27]- [29].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Image Enhancement Method based on a New Intensifier Operator

Yang¹,

Zenian²,

Zakaria³

2022

IJACSA

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In recent years, fuzzy image enhancement methods have been widely applied in image enhancement, which generally consists of three steps: fuzzification, modify membership(using intensifier (INT) operator), and defuzzification. This paper proposed a new INT operator used in fuzzy image enhancement. The INT operator is adjustable for different test images. The image enhancement method is as follows, firstly, calculate the image threshold (T ) using the OTSU method. Secondly, calculate pivotal point p corresponding to T , and find the corresponding INT operator function. Finally, use the INT operator in fuzzy Image Enhancement. The INT operator is used multiple times in the image processing process to obtain multiple result images. Comparative experiments show that the proposed new INT operator has better image enhancement effect when INT operator is applied at the same number of times. On the other hand, more intermediate process result images can also be obtained through the proposed new INT operator. More result images can provide material resources for the subsequent image processing.

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“…The commonly used gray transformation method are composed by direct stretching, quadratic function, sine function, complex function, and other nonlinear functions [12][13][14][15][16][17][18][19][20]. Fuzzy set has been applied effectively in image enhancement [21][22][23][24][25][26][27]. Image enhancement is more and more widely used in various fields.…”

mentioning

confidence: 99%

An Image Enhancement Method Using Nonlinear Function

Yang

Zenian

Zakaria

2022

IJEER

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Image enhancement plays an important role in image processing. This paper proposed an image enhancement method using a nonlinear(exponential) function. Firstly, we use the exponential function to construct the gray transformation equation. Then, depending on the position of each pixel, the gray scale obtained by gray transformation is further adjusted based on the compression factor. In section 3, the comparative experiment shows that the proposed method could achieve good performance.

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Fuzzy Image Enhancement Based on Algebraic Function and Cycloid Arc Length

Cited by 3 publications

References 11 publications

Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Fuzzy Image Enhancement Method based on a New Intensifier Operator

An Image Enhancement Method Using Nonlinear Function

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