Kartik Mittal

Papers

Total Citations

217

H-Index

About

Kartik Mittal is a researcher specializing in computer vision and deep learning, with a particular focus on underwater image enhancement and model compression. His most recognized contribution is the development of **Shallow-UWnet**, a lightweight yet highly effective convolutional neural network designed to enhance the quality of degraded underwater images. Published in 2021, this work addresses a critical challenge in underwater robotics and ocean engineering — producing visually clear imagery despite the distortions caused by light absorption and scattering in aquatic environments. What sets Mittal's approach apart is its emphasis on computational efficiency: rather than relying on resource-heavy deep CNNs or GANs, Shallow-UWnet achieves competitive enhancement performance through a compressed, shallow architecture, making it far more deployable in real-world, resource-constrained systems. The student abstract version of the paper alone has garnered over 192 citations, a remarkable achievement that underscores the significance and broad appeal of his work within the research community. Mittal's contributions highlight a growing and important direction in AI research — building models that are not only accurate but also practical and scalable for real-world applications in marine science and autonomous underwater vehicles.