An artificial bee colony algorithm for image contrast enhancement

Draa, Amer; Bouaziz, Amira

doi:10.1016/j.swevo.2014.01.003

Cited by 117 publications

(54 citation statements)

References 19 publications

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“…Entropy of input image is 4.76, GWO it is around 5.74 while GA have higher entropy 6.53. Thus the quality of the input image is 45.6 by using GA [1] quality is 73 and the GWO achieves 91.5.…”

Section: Simulation and Resultsmentioning

confidence: 98%

“…Considering contrast enhancement as an optimization problem gives rise to the necessity of defining two aspects: the representation of solutions and the objective function. A vectorial representation of each possible solutions has been used in [1] . The same representation is adopted in the present work.…”

Section: Basic Methodologymentioning

confidence: 99%

“…In this method the objective function is formed by combining three performance measures, namely entropy value, sum of edge intensities and number of edges. Quality of the image is given by F(Z) = log(log(E(I(Z)))) * ne(I(Z)) * H(I(Z)) (2) H((I(Z)) is the entropy of the output image Where intensity of enhanced image is E(I(x)) = ∑ ∑ √∂h 1 …”

Section: Image Parametrs Relationmentioning

confidence: 99%

“…Thus the image parameters such as Entropy, Number of edges, PSNR, Quality of input and enhanced image achieved by proposed GWO method along with GA [1] results. It is found from the table 1 and 2 that the proposed approach achieves better quality, PSNR and entropy values.…”

Section: Case1-lenamentioning

confidence: 99%

“…It is normally accomplished in the course of suppressing noise or by escalating contrast. Genetic Algorithm was used for optimal mapping of grey levels of the input image into new grey levels which offer better contrast for the image reference number, as in [1] .…”

Section: Introductionmentioning

confidence: 99%

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Grey Wolf Optimization Algorithm for Colour Image Enhancement Considering Brightness Preservation Constraint

Vinothini¹

2016

ijetst

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Section: Simulation and Resultsmentioning

confidence: 98%

Section: Basic Methodologymentioning

confidence: 99%

Section: Image Parametrs Relationmentioning

confidence: 99%

Section: Case1-lenamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Grey Wolf Optimization Algorithm for Colour Image Enhancement Considering Brightness Preservation Constraint

Vinothini¹

2016

ijetst

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Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

Chattopadhyay

Kundu

Singh

et al. 2021

Int J of Intelligent Sys

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Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and timeefficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of "Pneumonia" and "Normal" X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the

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A Differential Evolution Algorithm for Contrast Optimization

Oliveira

Britto

2020

Lecture Notes in Computer Science

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An artificial bee colony algorithm for image contrast enhancement

Cited by 117 publications

References 19 publications

Grey Wolf Optimization Algorithm for Colour Image Enhancement Considering Brightness Preservation Constraint

Grey Wolf Optimization Algorithm for Colour Image Enhancement Considering Brightness Preservation Constraint

Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

A Differential Evolution Algorithm for Contrast Optimization

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