Underwater Image

Underwater image enhancement aims to improve the quality of underwater images degraded by light absorption and scattering, enabling clearer visualization of underwater environments. Current research heavily focuses on deep learning approaches, employing various architectures like convolutional neural networks (CNNs), transformers, and diffusion models, often incorporating physical models of underwater light propagation for improved accuracy and realism. These advancements are crucial for applications such as autonomous underwater vehicle navigation, marine resource exploration, and ecological monitoring, significantly impacting fields like marine biology, oceanography, and robotics. The development of large synthetic datasets and novel algorithms that address the unique challenges of underwater imaging is a key area of ongoing investigation.