Toward Individual Tone Preference in Underwater Image Enhancement

Xu Liu, Yang Zhao, Kaichen Chi, Zhao Zhang, Yanxiang Chen, Wei Jia

Abstract: Underwater images often suffer from severe color distortion due to the challenging imaging environment. Underwater image enhancement (UIE) techniques have been developed to recover clear images, laying the foundation for various underwater research. However, existing UIE methods tend to produce fixed results without considering individual preferences for different color tones. And there is no dataset with ground truth (GT) in different tones. Therefore, we came up with the possibility of using the currently popular multimodal methods to control the color tone of enhanced images. This article proposes a method for generating underwater enhanced images with cold, warm, and normal tones using multimodal information supervision (MM-UIE). First, we leverage the relationship between text prompts and images to supervise the generation of cold or warm images. In addition, we introduce a 6-D color operator, which not only enhances the tone control of underwater images but also serves as a bridge between different tone images. Finally, we also found that multimodal supervision methods can not only control the color tone of underwater images but also improve the quality of underwater image generation. Experimental results demonstrate the superior performance of our method compared to state-of-the-art (SOTA) techniques. Our codes will be publicly available at https://github.com/perseveranceLX/MM-UIE.