Histogram Equalization Variants as Optimization Problems: A Review

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.

Automated summary

Histogram Equalization (HE) is a simple and widely used method for contrast enhancement in the consumer electronics field, but it may not be suitable as it flattens the histogram by distributing gray levels uniformly. To overcome this issue, various HE variants have been proposed based on proper histogram segmentation, histogram weighting, and range optimization techniques, although these modifications can sometimes become complex and computationally expensive.