A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain

Dhal, Krishna Gopal; Ray, Swarnajit; Das, Arunita; Das, Sanjoy

doi:10.1007/s11831-018-9289-9

Cited by 95 publications

(40 citation statements)

References 249 publications

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“…The proposed methods were tested on images taken from the ALL IDB dataset [15] and UCSB Bio-Segmentation Benchmark dataset [16,17]. [10,20], Edge based Contrast Measure (EBCM) [18], and Entropy [19]. C2G-SSIM [10,20] is a color to gray evaluation metric based on the popular image quality assessment metric SSIM.…”

Section: Resultsmentioning

confidence: 99%

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Decolorization of Digital Pathology Images: A Comparative Study

Dhal¹,

Ray²,

Das³

et al. 2019

StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference

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The major demerit of color to gray conversion is the loss of visually important image features. Digital pathology images are treated as the gold Standard for detection of various diseases, especially for the different types of cancer. Digital pathology images are color in nature, i.e. each pixel is a color vector represented by three values. Thus, the processing of these images requires high computational time. If these color images are converted into one dimensional gray images, then processing time can be reduced, which will help the Computer-Aided Diagnosis (CAD) system significantly for accurate classification and detection of different types of diseases. Therefore, this study focuses on the fast conversion of color digital pathology images into gray images. In order to do that, seven well established color to gray conversion, techniques have been employed for producing gray images with salient features. The outcomes have been validated visually and numerically.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, EBCM has been utilized to measure the edge information, as it is less sensitive to digitization effects and noise. Entropy [19] value reveals the information content in the image. If the distribution of the intensities is uniform, then it can be said that a histogram is equalized and the entropy of the image is more.…”

Section: Resultsmentioning

confidence: 99%

Decolorization of Digital Pathology Images: A Comparative Study

Dhal¹,

Ray²,

Das³

et al. 2019

StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…• ≤ , expresses a standard current percent relates to the system short circuit ratio for limiting ITHD value. Furthermore, there is another parameter (< 10 −2 ) that cannot be ignored in this research, the parameter stands for the error value between the desired power factor ( ) and the actually returned power factor ( ), the definition is indicated as formula (26).  = goal PF PF (26) With regards to the optimization objective, we introduce the parameter in [11] proposed by Biswas…”

Section: Constraints and Objective Function Formulationmentioning

confidence: 99%

“…In summary, SSA has certain strengths among the above algorithms. Therefore, SSA has been widely used in engineering applications [23], machine learning [24], image processing [25], and many other application fields [26][27]. In SSA, the population consists of a leader and followers.…”

Section: Introductionmentioning

confidence: 99%

An Improved Salp Swarm Algorithm With Spiral Flight Search for Optimizing Hybrid Active Power Filters’ Parameters

Zhang

et al. 2020

IEEE Access

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In view of various issues occasioned by harmonic pollution in the power system, the optimization of the filter parameters is of great significance. However, under the system constraints, estimating the filter parameters accurately and reliably is a challenging task. To complete this task, this paper proposes an improved salp swarm algorithm based on the spiral flight search strategy (ISSA-SFS), which rationally integrates the spiral flight search (SFS) strategy, the multiple leader (ML) strategy, and the random learning (RL) strategy with two improve evolution phases: the improved lead phase (ILP) and the improved follow phase (IFP). In the ILP, the SFS strategy is introduced to enhance the global search capability and avoid premature convergence. Furthermore, in the ILP, an ML strategy is proposed to select multiple leaders, further strengthening the global search capability of the proposed algorithm. In the IFP, a simple RL strategy is developed to learn two different random individuals, efficiently improving the local exploitation. The proposed algorithm ISSA-SFS is applied to optimize two prominent hybrid active power filter (HAPF) topologies, and each topology contains four different study cases. The overall experimental results indicate that the ISSA-SFS is a more promising alternative to achieve the optimal design of HAPFs compared with other well-established algorithms, especially in terms of accuracy and robustness. INDEX TERMS Parameter optimization, Harmonic pollution (HP), Hybrid active power filter (HAPF), Salp swarm algorithm (SSA), Spiral flight search (SFS).

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“…In this papers image constrast enhnacement is considered as an optimization problem and the artificial bee colony algorithm is utilized to find the optimal solution for this optimization problem. In the paper [65] can be found a survay on Nature-Inspired optimization algorithms and their application in image enhancement domain. Single image contrast enhancement methods are used to adjust the tone curve to correct the contrast of an input image.…”

Section: Latest Image Enhancement Techniquesmentioning

confidence: 99%

A Review on Image Enhancement Techniques

2019

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Image Enhancement is one of the most important and complex techniques in image processing technology. The main aim of image enhancement is to improve the visual appearance on an image and to offer a better representation of the image for Computer Vision Algorithms. In this paper,we is covered a few application fields of image enhancement with various images like grayscale, color, infrared and even with videos. The main objective of this paper is to highlight the drawbacks of the state of the art image enhancement techniques.

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A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain

Cited by 95 publications

References 249 publications

Decolorization of Digital Pathology Images: A Comparative Study

Decolorization of Digital Pathology Images: A Comparative Study

An Improved Salp Swarm Algorithm With Spiral Flight Search for Optimizing Hybrid Active Power Filters’ Parameters

A Review on Image Enhancement Techniques

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