Ankita Naik

Papers

Total Citations

217

H-Index

About

Ankita Naik is a researcher specializing in computer vision and deep learning, with a particular focus on underwater image enhancement — a critical challenge in underwater robotics and ocean engineering. Her most notable contribution, **Shallow-UWnet**, introduced a compressed neural network model designed to enhance degraded underwater imagery without relying on computationally expensive deep CNNs or GANs. By prioritizing efficiency and accessibility, her work addressed a significant gap between state-of-the-art performance and real-world deployability, making advanced image enhancement feasible for resource-constrained systems. The 2021 student abstract version of this work alone has garnered an impressive **192 citations**, reflecting the strong resonance of her research within the computer vision and marine technology communities. The accompanying full paper has further solidified the contribution's reach. What makes Naik's work particularly compelling is its practical orientation — bridging theoretical advancements with tangible applications in autonomous underwater vehicles and ocean exploration systems. Her ability to produce high-impact research at an early career stage marks her as a promising voice in the fields of efficient deep learning and environmental imaging.