Application of Deep Learning in Underwater Image Processing

Dear Colleagues,

Underwater image processing is one of the key technologies driving advancements in fields such as marine biology, oceanography, underwater exploration, and more. Served as a carrier of information, the quality of underwater images significantly impacts various applications. However, capturing high-quality underwater images is challenging due to complex and uncontrollable conditions. Common issues include color distortion, blurred details, low contrast and brightness, and noise. These problems hinder both human perception and practical applications. Furthermore, the unique properties of underwater imaging, such as its selective light absorption and scattering, make it difficult to achieve satisfactory results with the existing in-air methods or traditional underwater image processing techniques.

In recent years, deep learning technologies have emerged as a game-changer in addressing these challenges and improving underwater image quality. These technologies provide new opportunities and insights, enhancing their applicability and reliability in real-world scenarios. This Special Issue aims to bring together leading researchers and practitioners from around the world to showcase their latest research findings and future directions in this dynamic field. We particularly welcome the submissions responding to the call for proposals on underwater image processing and analysis that leverage advanced deep learning techniques.

The scope of this Special Issue is to cover all aspects that relate to underwater image processing. Topics of interest include, but are not limited to, the following:

• Underwater image enhancement and restoration;
• Underwater image denoising;
• Underwater object detection and classification;
• Underwater object tracking;
• Underwater object recognition;
• Underwater semantic segmentation;
• Underwater scene understanding;
• Underwater image depth estimation;
• Underwater 3D modeling;
• Underwater image synthesis and generation;
• Generative AI for underwater image processing;
• Underwater image quality assessment methods, including full-reference assessment metrics, non-reference assessment metrics, etc.

Prof. Dr. Chia-Hung Yeh
Prof. Dr. Chua-Chin Wang
Dr. Guo-Shiang Lin
Guest Editors

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