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Underwater Image Enhancement With Optimal Histogram Using Hybridized Particle Swarm and Dragonfly
The Computer Journal ( IF 1.4 ) Pub Date : 2021-05-26 , DOI: 10.1093/comjnl/bxab056
R Prasath 1 , T Kumanan 2
Affiliation  

Typically, underwater image processing is mainly concerned with balancing the color change distortion or light scattering. Various researches have been processed under these issues. This proposed model incorporates two phases, namely, contrast correction and color correction. Moreover, two processes are involved within the contrast correction model, namely: (i) global contrast correction and (ii) local contrast correction. For the image enhancement, the main target is on the histogram evaluation, and therefore, the optimal selection of histogram limit is very essential. For this optimization purpose, a new hybrid algorithm is introduced namely, swarm updated Dragonfly Algorithm, which is the hybridization of Particle Swarm Optimization (PSO) and Dragonfly Algorithm (DA). Further, this paper mainly focused on Integrated Global and Local Contrast Correction (IGLCC). The proposed model is finally distinguished over the other conventional models like Contrast Limited Adaptive Histogram, IGLCC, dynamic stretching IGLCC-Genetic Algorithm, IGLCC-PSO, IGLCC- Firefly and IGLCC-Cuckoo Search, IGLCC-Distance-Oriented Cuckoo Search and DA, and the superiority of the proposed work is proved.

中文翻译:

使用混合粒子群和蜻蜓的最佳直方图增强水下图像

通常,水下图像处理主要关注平衡颜色变化失真或光散射。针对这些问题进行了各种研究。该提出的模型包含两个阶段,即对比度校正和颜色校正。此外,对比度校正模型中涉及两个过程,即:(i)全局对比度校正和(ii)局部对比度校正。对于图像增强,主要目标是对直方图的评价,因此,直方图界限的最优选择是非常必要的。为此,引入了一种新的混合算法,即群更新蜻蜓算法,它是粒子群优化(PSO)和蜻蜓算法(DA)的混合。进一步,本文主要关注综合全局和局部对比度校正(IGLCC)。所提出的模型最终区别于其他传统模型,如对比度受限自适应直方图、IGLCC、动态拉伸 IGLCC-遗传算法、IGLCC-PSO、IGLCC-Firefly 和 IGLCC-Cuckoo Search、IGLCC-Distance-Oriented Cuckoo Search 和 DA,以及证明了所提工作的优越性。
更新日期:2021-05-26
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