Image Enhancement Using Fuzzy Logic Techniques

Mittal, Preeti; Saini, Rajesh K.; Jain, Neeraj

doi:10.1007/978-981-13-0589-4_50

Cited by 17 publications

(9 citation statements)

References 29 publications

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“…The FRBAM (Hanafiah et al, 2015;Zhao and Bose, 2002;Becerikli and Karan, 2005;Alshennawy and Ayman, 2009;Patel et al, 2011;Jena et al, 2019;M endez et al, 2019;Mittal et al, 2019;Nasibov et al, 2019;Abut and Soyguder, 2019;Iftikhar et al, 2019) mainly deals with the fuzzy rules that are if-then constraints. It is in the form of "if M then N", where M and N are considered as the premise and consequence of the rule respectively.…”

Section: Proposed Methodologymentioning

confidence: 99%

A fuzzy approach to identify fish red spot disease

Bhoi

Panda

Jena

et al. 2020

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PurposeFish are considered as one of the important aquatic animals in the planet. They play a vital role in the nutrient cycle. They can be considered as one of the healthy food for human beings. They can also act as a solution for some of the human health problems. If fish are affected by several diseases, they in turn provide an adverse effect on human health. Therefore, it is very much essential to protect fish from being affected by any diseases.Design/methodology/approachThis paper is mainly focused on the identification of the red spot diseased area in fish. In this work, a fuzzy rule based method (FRBAM) and triangular membership function (TMFN) is used to identify the red spot disease (RSD) in the fish by analyzing several red spot diseased fish (RSDF) images. The canny edge detector is used for intermediate processing of RSDF images.FindingsThe proposed method is able to identify the red pixels over the fish by marking the affected area with red color by using a standard RGB model.Originality/valueThe proposed method follows FRBAM and TMFN in order to detect the RSD and canny edge detector for processing of RSDF images. Finally, it is tested using ten different image sizes and the results show its better performance in terms of detection of RSD affected regions of fish and execution time.

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Section: Proposed Methodologymentioning

confidence: 99%

A fuzzy approach to identify fish red spot disease

Bhoi

Panda

Jena

et al. 2020

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“…The same argument is suitable for fuzzified images also. For further information to defuzzification refer [29] and [30]. For defuzzification of intuitionistic fuzzy sets and the crisp image which is get from fuzzified image will become the following expression…”

Section: Defuzzificationmentioning

confidence: 99%

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

Jebadass

Balasubramaniam

2022

Soft Comput

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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.

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“…The subband picture therefore exhibits high multiscality and multi-direction, thereby avoiding the Gibbs. NSCT transforms the two source pictures (𝐼 𝑖 ) and (𝐼 𝑣 ) with the following terms as ( 8) and (9).…”

Section: 𝑆(𝑖 𝑗) =mentioning

confidence: 99%

“…With its fuzzy logical reasoning, the fuzzy set theory gives a solution and theoretical basis to the fuzziness of images induced by uncertainty [8]. Given that this theory may be used to examine the visual features of pictures, fluctuating technology plays a key part in image processing, given the random nature and human visual properties it addresses [9]. The theory of fuzzy sets is classified by applying the membership of components to evaluate the extent of their affiliation [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

Khazal

Alhaleem

2022

IJEECS

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<span>Many types of medical pictures have to be fused, as single-modality medical images can give limited information because of the imagery and the complicated architecture of the human organ. This study proposes to offer a platform on which to make clinical diagnoses and to increase the accuracy of the target identification and the quality of the fused pictures by combining the benefits of nonsubsampled contourlet transform (NSCT) and fuzzy entropy. A picture is first broken down into low frequency or high frequency subbands through NSCT. In line with the various features of the low and high frequency components the respective fusion rules must be implemented. It calculates the level of membership of low frequency coefficients. The fusion of coefficients is also calculated and then utilized to retain picture features. By increasing regional energy, high-frequency components are merged. Inverse transformation produces the final fused picture. Experimental results have shown that, based on subjective visual effect and objective assessment standards, the suggested technique produces a satisfactory fusion effect. This process may also achieve high average gradient, standard deviation (SD), and edge preservation and maintain the fused picture features well. Effective reference can be provided by the outcome of the suggested algorithm for patients' assessment.</span>

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Image Enhancement Using Fuzzy Logic Techniques

Cited by 17 publications

References 29 publications

A fuzzy approach to identify fish red spot disease

A fuzzy approach to identify fish red spot disease

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

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

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