Document Type
Article
Publication Date
Winter 12-17-2023
Abstract
Many people have expressed an interest in underwater image processing in a variety of fields, including underwater vehicle control, archaeology, marine biological studies, etc. Underwater exploration is becoming an increasingly important element of our lives, with applications ranging from underwater marine and creature research to pipeline and communication logistics, military use, touristic and entertainment use. Underwater images suffer from poor visibility, distortion, and poor quality for a variety of causes, including light propagation. The major issue arises when these images must be captured at depths greater than 500 feet and artificial lighting needs to be provided. Efficient algorithms and models were proposed to enhance the images taken underwater. However, these networks challenge efficient real time deployment and needs significant computational resources and energy costs. In this paper these challenges will be tackled by using different network compression techniques such as pruning and quantization. These techniques will be applied on a model named WGH-net and the resulting in a tiny machine learning model is named tiny WGH-net model. Moreover, we compare the results of both models along with other models proposed by other researchers.
Recommended Citation
nagaty, Dr khaled; The British University in Egypt; and Pester, Andreas Dr, "Tiny Machine Learning for Underwater Image Enhancement: Pruning and Quantizaition Approach" (2023). Computer Science. 7.
https://buescholar.bue.edu.eg/comp_sci/7