基于多模態(tài)退化特征學(xué)習(xí)的水下圖像增強(qiáng)

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中圖分類號(hào)：TP391文獻(xiàn)標(biāo)志碼：A

Multi-modal Degradation Feature Learning for Underwater Image Enhancement

XIONG Qingbo 1 ，CHEN Lei 1 ， LIANG Xiaoli 1 ， LIU Tianxu2

（1. School of Software，Henan University，Kaifeng 45OO46，Henan，China; 2.Henan Provincial Transportation Dispatching Command Center，Zhengzhou 45Oo16，Henan，China）

Abstract：Toaddress the lack of generalizationand flexibilityin traditional underwater image enhancementmodels，a multi-modal degraded contrastive language-image pre-training（MD-CLIP）model was proposed.MD-CLIP model was trainedusingcontrastive learning toencodetheimage featuresand textfeaturesof low-qualityunderwaterimages into multi-modaldegraded features.Across-atentionmechanismand prompt embedding wereused to integrate themultimodal degraded featurespredictedbyMD-CLIP modelintotheunderwaterimageenhancementmodel，adjustingthe model's performance and generalization.Ablation and comparativeexperiments were conducted to validate the ffectivenessof themulti-modal degraded features.Theresultsshow that the multi-modal degraded featurespredicted by MD-CLIP model were embed into theunderwater image enhancement modelbyusing cross-atention mechanism，the image enhancement performanceand generalization performance of the model are significantlyimproved.MD-CLIP model can be added to other image enhancement models as a universal enhancement module.

Keywords：underwaterimage enancement；multi-modaldegradation feature;；contrastivelearning；cross-attentionmechanism

近年來(lái)，隨著海洋資源開發(fā)的興起，水下圖像增強(qiáng)技術(shù)備受關(guān)注。（剩余15082字）