Image Specular Highlight Removal using Generative Adversarial Network and Enhanced Grey Wolf Optimization Technique

Image highlight plays a major role in different interactive media and computer vision technology such as image fragmentation, recognition matching. The original data will be unclear if the contains highlights. Moreover, it may reduce robustness non-transparent well glassy objects also reduces accuracy. Hence, removal of highlights is an extremely crucial thing dome digital enhancement. This to develop enhancement texture imageries, video analytics. Several state-of-art methods are used for removing highlights; but they face some difficulties like insufficient efficacy, accuracy producing less datasets. To overcome this issue, paper proposes optimized GAN technology. Enhanced Grey Wolf Optimization (EGWO) technique employed feature selection process. Generative Adversarial Network machine learning (ML) algorithm. Here, two neural networks that compete among themselves produce better calculations. algorithm generates realistic data, especially images, with great practical results. investigational outcome reveals future has ability verify eliminate illumination spotlight so real details can obtained from image. effectiveness proposed work proved by comparing other existing models task. comparison gives 99.91% compared previous methods.