Mehnaz Ummar

Khalifa University of Science and Technology

Papers

Total Citations

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About

Mehnaz Ummar is an emerging researcher in the field of computer vision and deep learning, with a primary focus on autonomous underwater image enhancement. Her most-cited work, "Window-based transformer generative adversarial network for autonomous underwater image enhancement" (2023), has garnered 30 citations, showcasing her innovative approach to improving visual clarity in challenging underwater environments. By integrating transformer architectures with generative adversarial networks, Ummar addresses critical challenges in autonomous systems, such as low visibility and color distortion, which are vital for marine robotics and exploration. Her contributions advance the reliability of underwater imaging, directly impacting applications in environmental monitoring and deep-sea navigation. Though early in her career, Ummar’s work signals a promising trajectory in applied AI, blending cutting-edge neural network designs with real-world problem-solving. Her research not only enhances autonomous vehicle perception but also sets a foundation for future studies in domain-specific image restoration. For students and researchers, Ummar exemplifies how targeted, interdisciplinary work can drive meaningful progress in specialized computer vision tasks.