Prompt-UIE: A Unified Prompt-Driven Framework for Underwater Image Enhancement

Yanling Zhang, Linxuan Luo, Pan Mu, Cong Bai

Abstract: The complex and diverse underwater environment causes various types of degradation in underwater images. However, most existing methods focus on single underwater datasets, where the similarities in degradation limit the model’s exploration of different degradation characteristics. To address this challenge, we developed a new unified model for underwater image enhancement based on prompt learning, called PromptUIE, focusing on the common attributes of underwater images. Prompt-UIE is designed to adapt the pre-trained specific model to various underwater conditions without the need of multiple datasets. It builds upon a specific model and integrates a visual prompt module along with a reverse transmission map (RTM) guided loss function. First, the visual prompt module based on background light guides the specific model to perform enhancements based on different water types through a carefully designed visual prompt strategy. Next, the RTM guided loss function improves the model’s ability to handle non-uniform degradation. Experiments on both full-reference and no-reference datasets demonstrate the effectiveness and robustness of our method. The code is available at https://github.com/Zjut-MultimediaPlus/Prompt-UIE.

External IDs:dblp:conf/icassp/ZhangLMB25