scholarly journals Low contrast enhancement technique for color images using interval-valued intuitionistic fuzzy sets with contrast limited adaptive histogram equalization

2022 ◽  
Author(s):  
J. Reegan Jebadass ◽  
P. Balasubramaniam
Keyword(s):  
Fuzzy Sets ◽  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Intuitionistic Fuzzy Sets ◽  
Color Images ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

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

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.

Contrast Enhancement of Low-Contrast Medical Images Using Modified Contrast Limited Adaptive Histogram Equalization

2020 ◽  
Vol 10 (8) ◽  
pp. 1795-1803 ◽  
Author(s):  
Sajid Ali Khan ◽  
Shariq Hussain ◽  
Shunkun Yang
Keyword(s):  
Contrast Enhancement ◽  
High Frequency ◽  
Wavelet Transformation ◽  
Medical Images ◽  
Low Frequency ◽  
Histogram Equalization ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Image Details

The low contrast medical images seriously affect the clinical diagnosis process. To improve the image quality, we propose an effective medical images contrast enhancement technique in this paper. Shear wavelet transformation is used for decomposition of image components into low-frequency and high-frequency. The low-frequency part contrast is adjusted by applying modified contrast limited adaptive histogram equalization (CLAHE). The resultant image is further processed through technique of fuzzy contrast enhancement to maintain the spectral information of an image. Results of the experimentation show that our proposed technique enhance the image contrast up to a good degree while preserving the image details.

Width-based distance measures on interval-valued intuitionistic fuzzy sets

2021 ◽  
pp. 1-13
Author(s):  
Xi Li ◽  
Chunfeng Suo ◽  
Yongming Li
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Medical Diagnosis ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Intuitionistic Fuzzy ◽  
Interval Valued ◽  
Dissimilarity Function ◽  
Better Than

An essential topic of interval-valued intuitionistic fuzzy sets(IVIFSs) is distance measures. In this paper, we introduce a new kind of distance measures on IVIFSs. The novelty of our method lies in that we consider the width of intervals so that the uncertainty of outputs is strongly associated with the uncertainty of inputs. In addition, better than the distance measures given by predecessors, we define a new quaternary function on IVIFSs to construct the above-mentioned distance measures, which called interval-valued intuitionistic fuzzy dissimilarity function. Two specific methods for building the quaternary functions are proposed. Moreover, we also analyzed the degradation of the distance measures in this paper, and show that our measures can perfectly cover the measures on a simpler set. Finally, we provide illustrative examples in pattern recognition and medical diagnosis problems to confirm the effectiveness and advantages of the proposed distance measures.

On a Relationship Between Fuzzy Measures and AIFS

2016 ◽  
Vol 24 (06) ◽  
pp. 847-858 ◽  
Author(s):  
VicenÇ Torra ◽  
Yasuo Narukawa ◽  
Ronald R. Yager
Keyword(s):  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Fuzzy Measures ◽  
Intuitionistic Fuzzy ◽  
Atanassov Intuitionistic Fuzzy Sets ◽  
Interval Valued

The literature discusses several extensions of fuzzy sets. AIFS, IVFS, HFS, type-2 fuzzy sets are some of them. Interval valued fuzzy sets is one of the extensions where the membership is not a single value but an interval. Atanassov Intuitionistic fuzzy sets, for short AIFS, are defined in terms of two values for each element: membership and non-membership. In this paper we discuss AIFS and their relationship with fuzzy measures. The discussion permits us to define counter AIFS (cIFS) and discretionary AIFS (dIFS). They are extensions of fuzzy sets that are based on fuzzy measures.

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