Underwater Video Enhancement

Underwater video enhancement aims to improve the quality of underwater recordings, hampered by factors like light absorption and scattering, to enable clearer observation and analysis of marine environments. Recent research focuses on developing efficient deep learning models, often based on U-Net architectures, that leverage both intra- and inter-frame information for improved speed and accuracy in real-time video processing. The availability of large, high-resolution benchmark datasets is driving progress, enabling the training of more robust and effective enhancement algorithms with significant implications for marine biology, underwater robotics, and exploration.