Shallow-UWnet: Compressed Model for Underwater Image Enhancement (Student Abstract)

Authors

  • Ankita Naik University of Massachusetts Amherst
  • Apurva Swarnakar University of Massachusetts Amherst
  • Kartik Mittal University of Massachusetts Amherst

Keywords:

Model Compression And Acceleration, Underwater Image Enhancement, Low-light Image Enhancement, Convolutional Neural Network

Abstract

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.

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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. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17923

Issue

Section

AAAI Student Abstract and Poster Program