Taslim Mahbub

Khalifa University of Science and Technology

Papers

Total Citations

H-Index

About

Taslim Mahbub is a leading researcher in computer vision and deep learning, with a primary focus on autonomous underwater image enhancement. Their most cited work, "Window-based transformer generative adversarial network for autonomous underwater image enhancement" (2023), has garnered 30 citations, reflecting its significant impact on improving visual data quality in challenging aquatic environments. Mahbub's key contributions lie in developing novel generative adversarial network architectures that integrate transformer mechanisms to address issues like color distortion, low contrast, and blurring in underwater imagery—critical for applications in marine robotics, environmental monitoring, and autonomous navigation. By pioneering window-based attention strategies, they have advanced the state-of-the-art in real-time image restoration, enabling more reliable perception for autonomous underwater vehicles. Their work bridges the gap between theoretical deep learning and practical deployment in extreme conditions, earning recognition for its innovation and applicability. Mahbub's research continues to influence the fields of underwater computer vision and AI-driven environmental sensing, making them a notable figure in the intersection of generative models and marine technology.