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 and matching. The original data will be unclear if the image contains highlights. Moreover, it may reduce the robustness in non-transparent as well as glassy obje...

Full description

Saved in:
Bibliographic Details
Published in:International journal of advanced computer science & applications 2023, Vol.14 (6)
Main Authors: Reddy, Maddikera Krishna, Sekhar, J. C., Rao, Vuda Sreenivasa, Ansari, Mohammed Saleh Al, El-Ebiary, Yousef A.Baker, Ramu, Jarubula, Manikandan, R.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer vision
Digital imaging
Generative adversarial networks
Image enhancement
Machine learning
Neural networks
Optimization
Optimization techniques
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Image highlight plays a major role in different interactive media and computer vision technology such as image fragmentation, recognition and matching. The original data will be unclear if the image contains highlights. Moreover, it may reduce the robustness in non-transparent as well as glassy objects and also it reduces accuracy. Hence, the removal of highlights is an extremely crucial thing in the dome of digital image enhancement. This is to develop the enhancement of the texture in imageries, and video analytics. Several state-of-art methods are used for removing highlights; but they face some difficulties like insufficient efficacy, accuracy and producing less datasets. To overcome this issue, this paper proposes an optimized GAN technology. The Enhanced Grey Wolf Optimization (EGWO) technique is employed for feature selection process. Generative Adversarial Network is a machine learning (ML) algorithm. Here, two neural networks that will compete among themselves to produce better calculations. The algorithm generates realistic data, especially images, with great practical results. The investigational outcome reveals that the future algorithm has the ability to verify and eliminate the illumination spotlight in the image so that real details can be obtained from the image. The effectiveness of the proposed work can be proved by comparing the proposed optimized GAN with other existing models in highlight removal task. The comparison outcome gives better accuracy with 99.91% compared to previous existing methods.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2023.0140668