A hybrid flower pollination optimization based modified multi-scale retinex for blood cell microscopic image enhancement

Shahd T. Mohamed, H. M. Ebeid, A. Hassanien, M. Tolba
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引用次数: 6

Abstract

Multi-Scale Retinex (MSR) algorithm enhances images that are taken in nonlinear lighting conditions. In this paper, we propose an automated approach for image enhancement using MSR and Flower Pollination Algorithm (FPA) to select the optimal weights to the different scales of Gaussian filters from the desired image for MSR. The experiments are carried out using blood cell microscopic imaging to investigate the MSR and FPA. The proposed method are compared against the state-of-the-art swarms algorithms; Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo search (CS) and standard MSR in the aspect of the mean, standard deviation (SD), peak to signal-to-noise ratio (PSNR) and the root mean square error (RMSE). The experiment results showed that the proposed hybrid algorithm proves itself to be robust and effective through experimental results and outperforms the state-of-the-art algorithms.
基于改进多尺度视网膜的杂交花授粉优化血细胞显微图像增强
多尺度Retinex (MSR)算法可以增强在非线性光照条件下拍摄的图像。在本文中,我们提出了一种使用MSR和花授粉算法(FPA)的自动图像增强方法,从MSR所需的图像中选择不同尺度高斯滤波器的最优权重。实验采用血细胞显微成像技术研究了微磁共振和FPA。将该方法与最先进的群算法进行了比较;粒子群算法(PSO)、人工蜂群算法(ABC)、杜鹃搜索算法(CS)和标准MSR算法在均值、标准差(SD)、峰值信噪比(PSNR)和均方根误差(RMSE)方面进行了比较。实验结果表明,本文提出的混合算法鲁棒性好、有效性好,优于现有的混合算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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