In the consumer electronics field, the main challenge in image processing is to preserve the original brightness. Histogram Equalization (HE) is one of the simplest and widely used methods for contrast enhancement. However, HE does not suit into the consumer electronics field as this procedure flattens the histogram by distributing the entire gray levels uniformly. Therefore, several HE variants have been proposed based on proper histogram segmentation, histogram weighting, and range optimization techniques to overcome this flattening effect. However, sometimes these modifications become complex and computationally expensive. Recently, researchers have formulated the HE variants for image enhancement as optimization problems and solved, using Nature-Inspired Optimization Algorithms (NIOA), which starts a new era in the image enhancement field. This study presents an up-to-date review over the application of NIOAs for HE variants in image enhancement domain. The main issues which are involved in the application of NIOAs with HE are also discussed here.

在消费电子领域，图像处理的主要挑战是保持原始亮度。直方图均衡化（HE）是最简单且广泛使用的对比度增强方法之一。但是，HE不适合消费电子领域，因为此过程通过均匀地分布整个灰度来使直方图变平。因此，基于适当的直方图分割，直方图加权和范围优化技术，已提出了几种HE变体，以克服这种变平效果。但是，有时这些修改变得复杂且计算量大。最近，研究人员已将用于图像增强的HE变体公式化为优化问题，并使用自然启发式优化算法（NIOA）解决了这一问题，从而开启了图像增强领域的新纪元。这项研究提出了有关NIOAs在图像增强领域中HE变体的应用的最新综述。在此还讨论了将NIOAs与HE一起使用时涉及的主要问题。