The ability of underwater robots to accurately perceive their surroundings relies heavily on high-quality imaging systems. However, capturing clear images in aquatic environments is difficult due to light absorption and scattering challenges. Numerous studies have been conducted to develop underwater image enhancement techniques to address this issue, but striking a balance between computational speed, enhancement effect, and robustness remains a significant challenge. Our research takes a unique approach by analyzing the degradation of standard colors and utilizing the degradation of white as a priori information for our proposed adaptive color restoration and histogram equalization method. By modeling the difference in white color between air and underwater images, we estimate compensation coefficients via optimization to restore the color of underwater images. Our method achieves a superior balance of computational speed, color enhancement effect, and robustness compared with other state-of-the-art methods, as demonstrated by our experiments in various sea areas. This research significantly advances our understanding of underwater imaging and provides a practical solution for enhancing underwater images.

Advances in modern deep learning-based computer vision perception techniques have revolutionized animal movement research methods. These techniques have also opened up new avenues for studying fish swimming. To that end, we have developed a visual perception system based on pose estimation to analyze fish swimming. Our system can quantify fish motion by 3D fish pose estimation and dynamically visualize the motion data of marked keypoints. Our experimental results show that our system can accurately extract the motion characteristics of fish swimming, which analyze how fish bodies and fins work together during different swimming states. This research provides an innovative idea for studying fish swimming, which can be valuable in designing, developing, and optimizing modern underwater robots, especially multi-fin co-driven bionic robotic fish. The code and dataset are available at https://github.com/wux024/AdamPosePlug.