Underwater image super-resolution using improved SRCNN

Ryosuke Horimachi; Huimin Lu; Yuchao Zheng; Tohru Kamiya; Yoshihisa Nakatoh; Seiichi Serikawa

doi:10.1117/12.2655051

22 December 2022 Underwater image super-resolution using improved SRCNN

Ryosuke Horimachi, Huimin Lu, Yuchao Zheng, Tohru Kamiya, Yoshihisa Nakatoh, Seiichi Serikawa

Proceedings Volume 12508, International Symposium on Artificial Intelligence and Robotics 2022; 125080C (2022) https://doi.org/10.1117/12.2655051
Event: Seventh International Symposium on Artificial Intelligence and Robotics 2022, 2022, Shanghai, China

Abstract

In recent years, industrialization and economic development in countries around the world have led to an ever-increasing demand for energy. Renewable energies are attracting attention, but they still often use mineral resources such as coal, petroleum, and natural gas, and onshore resources are depleting day by day. These energy and metal resources, such as copper, support Japan's industries and affluent lifestyle, and if Japan continues to rely on imports for most of these resources, it will become difficult for Japan to secure a stable supply of these energies and resources. Therefore, mining of mineral resources on the seafloor is essential to solve these problems, and research on seafloor resource surveys and mining is underway. Because direct human exploration and mining of seafloor resources are naturally dangerous, underwater robots are used to explore and mine seafloor resources. However, due to light absorption and turbidity in water, the underwater image of an underwater robot is sometimes less visible, making exploration unsatisfactory. Therefore, there is a need for higher-resolution underwater images of underwater robots. In this study, we perform super-resolution of underwater images using an improved SRCNN to support research on underwater images of underwater robots. The conventional SRCNN method uses the ReLU function as the activation function, but the improved SRCNN uses the PReLU function and FReLU function, which are extended activation functions of the ReLU function, to improve accuracy.

Citation Download Citation

Ryosuke Horimachi, Huimin Lu, Yuchao Zheng, Tohru Kamiya, Yoshihisa Nakatoh, and Seiichi Serikawa "Underwater image super-resolution using improved SRCNN", Proc. SPIE 12508, International Symposium on Artificial Intelligence and Robotics 2022, 125080C (22 December 2022); https://doi.org/10.1117/12.2655051

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KEYWORDS

Super resolution

Robots

Minerals

Mining

Data modeling

Convolution

Solar energy

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