Underwater Image Enhancement

Underwater image enhancement aims to improve the quality of underwater images degraded by light absorption and scattering, enabling clearer visualization and analysis of underwater environments. Current research heavily utilizes deep learning, 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 improving the performance of autonomous underwater vehicles (AUVs), marine research, and other applications reliant on underwater vision systems, particularly in enhancing object detection and other high-level vision tasks. The field is also actively exploring efficient, lightweight models suitable for real-time processing on resource-constrained platforms.