Image Enhancement Using Tri-Histogram Equalization Technique

Histogram equalization (HE) is essential for correcting digital picture contrast. Conventional HE methods appear weird due to extreme artifacts and brightness changes. Because of its simplicity and effectiveness, histogram equalization is used to increase contrast in many applications. Medical image processing and radar are examples. Due to histogram normalization's flattening, equalization may modify picture brightness. Equalizing may significantly change a picture's brightness; hence video systems never used it. In this paper, a mean and median boundary-oriented equalization technique has been implemented named as image enhancement using tri-histogram equalization technique(IETHE) to improve the picture quality along with brightness control. As per this technique, the input image histogram is partitioned into three sub-histograms using separation threshold settings. The average of mean and median are taken to determine sub-histogram border criteria. The enhanced image is created using transformation function after correcting the sub-histograms. The recommended method boosts visual contrast and control the artifacts and brightness. Both visual quality ratings and quantitative measures like entropy index and PSNR reveal that the recommended strategy beats other techniques.