Image improvement is the process to improve visual quality, from the original image to get optimal image results. The first category, this technique operates on the transformation of frequency selection and the second technique operates directly at the pixel level of the image. in this study the Exposure Sub-Image Histogram Equalization (ESIHE) technique will be enhanced with a brightness level to get visual image results. Then ESIHE is compared with other additional techniques, such as Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) from the side of the image. Furthermore, for the even distribution of histograms, we will use ESIHE entropy calculations to show an increase in the image results that are more optimal when compared with HE and AHE. The visual image quality of each technique shows the strength of the method and the superiority of the other methods for various types of images.
