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Underwater Vision Sensing System: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 613

Special Issue Editors

College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: underwater vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Division of Integrative Systems and Design (ISD) and Department of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology, Hong Kong 999077, China
Interests: underwater vision; marine vision; underwater scene understanding
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical and Control Engineering, Faculty of Engineering, Kyushu Institute of Technology, Kitakyushu 804-0015, Japan
Interests: robotics; oceanic optics; computer vision; artificial Intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image and video sensing are essential tools for exploring and understanding the hidden world beneath the waves, yet the complex aquatic environment has prevented them from being widely deployed. Recent advances in underwater vision sensing technology have enabled researchers to develop efficient and cost-effective systems for underwater imaging. Underwater vision sensing systems can be used for a variety of applications, such as underwater surveillance and tracking, the monitoring of marine habitats and species, navigation and mapping, and exploration.

The goal of this Special Issue is to introduce recent advances in underwater vision sensing systems, which involve autonomous underwater vehicles, sonar imaging, optical imaging, 3D reconstruction, automatic driving devices, sonar system optimization, the Internet of Things, security facilities, navigation systems, computer vision devices, acoustic materials, acoustic technologies, and so on. In this Special Issue, we expect to publish papers with theoretical and practical innovations in underwater vision sensing systems involving underwater optical and sonar imaging, underwater sensor design and development, image and signal processing for underwater vision sensing, image and signal analysis for object recognition and tracking, machine learning and artificial intelligence for underwater vision sensing, underwater navigation and localization, underwater communication and networking, applications of underwater vision sensing in autonomous underwater vehicles, and any other possible applications.

Topics of interest include, but are not limited to, the following:

  • Underwater imaging;
  • Acoustic technology;
  • The implementation of deep learning in underwater vision systems;
  • Underwater 3D reconstruction;
  • Remotely operated vehicles;
  • Autonomous underwater vehicles;
  • Underwater stereo vision;
  • Underwater monitoring;
  • Image and signal processing in underwater vision sensing systems;
  • Underwater networks;
  • Underwater communication;
  • Underwater sensors;
  • Underwater materials;
  • Underwater devices;
  • Underwater navigation;
  • Underwater sensing and detection;
  • Underwater microscopy.

Dr. Zhibin Yu
Dr. Sai-Kit Yeung
Dr. Huimin Lu
Guest Editors

Manuscript Submission Information

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Keywords

  • underwater imaging
  • underwater 3D reconstruction
  • autonomous underwater vehicles
  • underwater vision sensing systems
  • underwater networks
  • underwater communication
  • underwater sensing and detection
  • underwater microscopy

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Published Papers (2 papers)

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18 pages, 4913 KiB  
Article
Enhancing Three-Dimensional Reconstruction Through Intelligent Colormap Selection
by Alexandros Vrochidis, Dimitrios Tzovaras and Stelios Krinidis
Sensors 2025, 25(8), 2576; https://doi.org/10.3390/s25082576 - 18 Apr 2025
Viewed by 199
Abstract
Photogrammetry is the process of creating three-dimensional (3D) reconstructions from two-dimensional images. In this paper, a novel method is introduced that significantly enhances 3D reconstruction by improving image quality through a combination of RGB stretching, Contrast Limited Adaptive Histogram Equalization (CLAHE), and colormaps. [...] Read more.
Photogrammetry is the process of creating three-dimensional (3D) reconstructions from two-dimensional images. In this paper, a novel method is introduced that significantly enhances 3D reconstruction by improving image quality through a combination of RGB stretching, Contrast Limited Adaptive Histogram Equalization (CLAHE), and colormaps. This approach effectively increases the number of keypoints and matches between images, resulting in more accurate and detailed 3D reconstructions. Additionally, a heuristic methodology is proposed to identify the optimal colormaps for each dataset based on keypoint matches between image pairs. This approach saves valuable time by recommending the most effective colormaps, eliminating the need to test each individually. A new dataset comprising two real-world image collections from underwater cultural heritage sites is presented to validate the algorithm, characterized by challenging environments such as low visibility and varying lighting conditions. The limitations of existing techniques are addressed by this method, providing a robust solution for enhancing image quality in demanding underwater scenarios. Experimental results show that image enhancement can lead to a 7.91% and 11.4% improvement, and the enhancement fusion with colormaps improves reconstruction accuracy by 10.82% and 64.11%. These findings render the proposed methodology a promising tool for 3D reconstruction in challenging environments, like underwater. Full article
(This article belongs to the Special Issue Underwater Vision Sensing System: 2nd Edition)
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14 pages, 13883 KiB  
Article
CASF-Net: Underwater Image Enhancement with Color Correction and Spatial Fusion
by Kai Chen, Zhenhao Li, Fanting Zhou and Zhibin Yu
Sensors 2025, 25(8), 2574; https://doi.org/10.3390/s25082574 - 18 Apr 2025
Viewed by 277
Abstract
With the exploration and exploitation of marine resources, underwater images, which serve as crucial carriers of underwater information, significantly influence the advancement of related fields. Despite dozens of underwater image enhancement (UIE) methods being proposed, the impacts of insufficient contrast and distortion of [...] Read more.
With the exploration and exploitation of marine resources, underwater images, which serve as crucial carriers of underwater information, significantly influence the advancement of related fields. Despite dozens of underwater image enhancement (UIE) methods being proposed, the impacts of insufficient contrast and distortion of surface texture during UIE are currently underappreciated. To address these challenges, we propose a novel UIE method, channel-adaptive and spatial-fusion Net (CASF-Net), which uses a network channel-adaptive correction module (CACM) to enhance feature extraction and color correction to solve the problem of insufficient contrast. In addition, the CASF-Net utilizes a spatial multi-scale fusion module (SMFM) to solve the surface texture distortion problem and effectively improve underwater image saturation. Furthermore, we propose a Large-scale High-resolution Underwater Image Enhancement Dataset (LHUI), which contains 13,080 pairs of high-resolution images with sufficient diversity for efficient UIE training. Experimental results show that the proposed network design performs well in the UIE task compared with existing methods. Full article
(This article belongs to the Special Issue Underwater Vision Sensing System: 2nd Edition)
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