Shallow-UWnet: Compressed Model for Underwater Image Enhancement (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v35i18.17923Keywords:
Model Compression And Acceleration, Underwater Image Enhancement, Low-light Image Enhancement, Convolutional Neural NetworkAbstract
Over the past few decades, underwater image enhancement has attracted an increasing amount of research effort due to its significance in underwater robotics and ocean engineering. Research has evolved from implementing physics-based solutions to using very deep CNNs and GANs. However, these state-of-art algorithms are computationally expensive and memory intensive. This hinders their deployment on portable devices for underwater exploration tasks. These models are trained on either synthetic or limited real-world datasets making them less practical in real-world scenarios. In this paper, we propose a shallow neural network architecture, Shallow-UWnet which maintains performance and has fewer parameters than the state-of-art models. We also demonstrated the generalization of our model by benchmarking its performance on a combination of synthetic and real-world datasets.Downloads
Published
2021-05-18
How to Cite
Naik, A., Swarnakar, A., & Mittal, K. (2021). Shallow-UWnet: Compressed Model for Underwater Image Enhancement (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15853-15854. https://doi.org/10.1609/aaai.v35i18.17923
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Section
AAAI Student Abstract and Poster Program