论文标题
使用COOT优化算法进行分割和参数选择的子图像直方图均衡
Sub-Image Histogram Equalization using Coot Optimization Algorithm for Segmentation and Parameter Selection
论文作者
论文摘要
在以客观的方式评估图像方面，对比度增强非常重要。对比度增强对于各种算法也很重要，包括对样品进行准确分类的监督和无监督算法。一些对比增强算法通过解决低对比度问题来解决此问题。基于均值和方差的子图像直方图均衡（MVSIHE）算法是文献中提出的这些对比增强方法之一。它具有不同的参数，需要调整以达到最佳结果。在这项研究中，我们采用了最新的优化算法之一，即COOT优化算法（COA）为MVSIHE算法选择适当的参数。盲/无引用的图像空间质量评估器（Brisque）和自然图像质量评估器（NIQE）指标用于评估COOT群体的适应性。结果表明，所提出的方法可用于生物医学图像处理领域。
Contrast enhancement is very important in terms of assessing images in an objective way. Contrast enhancement is also significant for various algorithms including supervised and unsupervised algorithms for accurate classification of samples. Some contrast enhancement algorithms solve this problem by addressing the low contrast issue. Mean and variance based sub-image histogram equalization (MVSIHE) algorithm is one of these contrast enhancements methods proposed in the literature. It has different parameters which need to be tuned in order to achieve optimum results. With this motivation, in this study, we employed one of the most recent optimization algorithms, namely, coot optimization algorithm (COA) for selecting appropriate parameters for the MVSIHE algorithm. Blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE) metrics are used for evaluating fitness of the coot swarm population. The results show that the proposed method can be used in the field of biomedical image processing.
