scholarly journals Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized By Artificial Bee Colony Algorithm

2017 ◽  
Vol 8 (1) ◽  
pp. 7
Author(s):  
Daniel M. Wonohadidjojo
Keyword(s):  
Fuzzy Sets ◽  
Contrast Enhancement ◽  
Artificial Bee Colony ◽  
Histogram Equalization ◽  
Intuitionistic Fuzzy Sets ◽  
Local Contrast ◽  
Intuitionistic Fuzzy ◽  
Bee Colony ◽  
Local Contrast Enhancement ◽  
Equalization Method

The article presented the enhancement method of cells images. The first method used in the local contrast enhancement was Intuitionistic Fuzzy Sets (IFS). The proposed method is the IFS optimized by Artificial Bee Colony (ABC) algorithm. The ABC was used to optimize the membership function parameter of IFS. To measure the image quality, Image Enhancement Metric (IEM)was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method.

scholarly journals Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram

2019 ◽  
Vol 11 (11) ◽  
pp. 1381 ◽  
Author(s):  
Chengwei Liu ◽  
Xiubao Sui ◽  
Xiaodong Kuang ◽  
Yuan Liu ◽  
Guohua Gu ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Noise Suppression ◽  
Mapping Function ◽  
Visual Quality ◽  
Histogram Equalization ◽  
The Other ◽  
Local Contrast ◽  
Infrared Images ◽  
Local Contrast Enhancement ◽  
Block Based

In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.

scholarly journals Low Contrast Enhancement Technique for Color Images using Interval-Valued Intuitionistic Fuzzy Sets with Contrast Limited Adaptive Histogram Equalization

2021 ◽  
Author(s):  
J Reegan Jebadass ◽  
P Balasubramaniam
Keyword(s):  
Fuzzy Sets ◽  
Histogram Equalization ◽  
Intuitionistic Fuzzy Sets ◽  
Significant Shift ◽  
Low Light ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Histogram ◽  
Interval Valued

Abstract This work introduces a program to enhance images taken in low light. Fuzzy set theory is creating a significant shift in image processing. Interval-valued intuitionistic fuzzy sets (IVIFS) based on intuitionistic fuzzy sets constructed from fuzzy sets are used to enhance images taken in low light. In the proposed method, first the given low light image is fuzzified by normal fuzzification. Then the fuzzified image is converted to an interval-valued intuitionistic fuzzy image. This image will be proposed enhanced image after applying the contrast limited adaptive histogram equalization (CLAHE). The experimental results reveal that the proposed method gives better results when compared with other existing methods like histogram equalization (HE), CLAHE, brightness preserving dynamic fuzzy histogram equalization (BPDFHE), histogram specification approach (HSA). Based on the performance analysis like entropy and correlation coefficient (CC), the proposed method gives better results.Mathematics Subject Classification (2010) 68U10 · 94D05

One Hundredth of Intuitionistic Fuzzy Sets

2019 ◽  
Vol 10 (3) ◽  
pp. 445-453
Author(s):  
R. Nagalingam ◽  
S. Rajaram
Keyword(s):  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Intuitionistic Fuzzy

Probability Theory for Fuzzy Quantum Spaces with Statistical Applications

2017 ◽  
Author(s):  
Renáta Bartková ◽  
Beloslav Riečan ◽  
Anna Tirpáková
Keyword(s):  
Probability Theory ◽  
Fuzzy Sets ◽  
Ergodic Theorem ◽  
Intuitionistic Fuzzy Sets ◽  
Mathematics Students ◽  
Intuitionistic Fuzzy ◽  
Individual Ergodic Theorem ◽  
Recurrence Theorem ◽  
The Individual ◽  
Atanassov Intuitionistic Fuzzy Sets

The reference considers probability theory in two main domains: fuzzy set theory, and quantum models. Readers will learn about the Kolmogorov probability theory and its implications in these two areas. Other topics covered include intuitionistic fuzzy sets (IF-set) limit theorems, individual ergodic theorem and relevant statistical applications (examples from correlation theory and factor analysis in Atanassov intuitionistic fuzzy sets systems, the individual ergodic theorem and the Poincaré recurrence theorem). This book is a useful resource for mathematics students and researchers seeking information about fuzzy sets in quantum spaces.

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