Muhammad Tariq Mahmood

Korea University of Technology and Education

Papers

Total Citations

207

H-Index

About

Muhammad Tariq Mahmood is a leading researcher in computer vision and deep learning, with a primary focus on human activity recognition and underwater image restoration. His most impactful work, "Robust Human Activity Recognition Using Multimodal Feature-Level Fusion" (2019), has garnered 184 citations, establishing a foundational approach for integrating diverse sensor data to enhance automated surveillance, robotics, and health monitoring systems. In the domain of underwater imaging, Mahmood has pioneered innovative solutions to correct color distortion and restore visual clarity in challenging aquatic environments. His 2024 paper introducing UW-Net, an end-to-end neural network model for underwater image restoration, addresses critical needs in marine biology, underwater robotics, and rescue operations. Additionally, his 2022 work on regularization of coherent structures further refines physics-based optimization methods for underwater image enhancement. Through these contributions, Mahmood has demonstrated a remarkable ability to bridge theoretical deep learning models with practical, real-world applications, significantly advancing the reliability of computer vision systems in both terrestrial and underwater settings. His research continues to inspire new approaches in multimodal fusion and environmental imaging.