The performance evaluation of the Cat and Particle Swarm Optimization Techniques in the image enhancement

Cam, Hilmi Bilal; Akcakoca, Salim; Elbir, Ahmet; İlhan, Hamza Osman; Aydin, Nizamettin

doi:10.1109/ebbt.2018.8391430

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…They reported that BFA yielded better performance metrics than GA. CSO, inspired by the behaviors of tracing and seeking mode of the cats, was first presented by Chu et al [23]. Çam et al [24] reported that CSO is faster than PSO in IE but lags in terms of Structural Similarity Index (SSIM).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In seeking mode, parameters represent the state of a cat visually scanning its environment to decide on its next destination. In tracing mode, parameters depict a cat as it tracks a target and moves closer to it [23,24,38,39]. In the context of HS, it can adaptively select the appropriate mode to locate the optimal stretching parameters.…”

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

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The Evaluation of Nature-Inspired Optimization Techniques for Contrast Enhancement in Images: A Novel Software Tool

Ilhan,

Elbir,

Serbes

et al. 2023

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This study is rooted in the direct correlation between the performance of multivariate techniques and the selection of parameters. The complexity and time-consuming nature of parameter selection, due to the need for exhaustive testing of all available parameters for optimal results, is acknowledged. To mitigate this issue, a novel software tool, integrating nine nature-inspired optimization methods (Differential Evolution, Artificial Bee Colony, Particle Swarm, Cat Swarm, Dragonfly, Black Hole, Bacterial Foraging, Genetic Algorithms, and Simulated Annealing), is proposed. These methods are employed in histogram stretching, a parameter-dependent contrast enhancement technique, with multiplication, addition, and root extraction operations as the target parameters for optimization. In addition to this, histogram equalization, a parameter-independent contrast enhancement technique, is included for the purpose of comparative performance analysis. The software tool, publicly available, provides four performance metrics namely, Mean Square Error, Peak Signal-to-noise Ratio, Structural Similarity Index, and processing times. A rigorous evaluation using the widely recognized Tampere Image dataset indicates that Differential Evolution emerged as the most efficient technique, scoring highest for Structural Similarity Index (0.948) and second best for Mean Square Error (278.05) and Peak Signal to Noise Ratio (26.962). Furthermore, Particle Swarm Optimization demonstrated the fastest time complexity, requiring merely 0.6 sec per image for parameter definition. Notably, it was observed that while histogram equalization tends to degrade original images, the adaptive nature of optimized histogram stretching remains preserved, thereby leaving the image quality unaffected. Such findings highlight the efficacy of the proposed software tool in the optimization and evaluation of contrast enhancement techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Cat Swarm Optimization (Cso)mentioning

confidence: 99%

The Evaluation of Nature-Inspired Optimization Techniques for Contrast Enhancement in Images: A Novel Software Tool

Ilhan,

Elbir,

Serbes

et al. 2023

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Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed¹,

Rashid²,

Saeed³

2020

Preprint

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This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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The performance evaluation of the Cat and Particle Swarm Optimization Techniques in the image enhancement

Cited by 2 publications

References 10 publications

The Evaluation of Nature-Inspired Optimization Techniques for Contrast Enhancement in Images: A Novel Software Tool

The Evaluation of Nature-Inspired Optimization Techniques for Contrast Enhancement in Images: A Novel Software Tool

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

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