In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation, posing significant challenges to image enhancement (IE) and object detection (OD) tasks. Currently, in-air image enhancement and detection methods have made notable progress, but their application in underwater conditions is limited due to the complexity and variability of these environments. Fine-tuning in-air models saves high overhead and has more optional reference work than building an underwater model from scratch. To address these issues, we design a transfer plugin with multiple priors for converting in-air models to underwater applications, named IA2U. IA2U enables efficient application in underwater scenarios, thereby improving performance in Underwater IE and OD. IA2U integrates three types of underwater priors: the water type prior that characterizes the degree of image degradation, such as color and visibility; the degradation prior, focusing on differences in details and textures; and the sample prior, considering the environmental conditions at the time of capture and the characteristics of the photographed object. Utilizing a Transformer-like structure, IA2U employs these priors as query conditions and a joint task loss function to achieve hierarchical enhancement of task-level underwater image features, therefore considering the requirements of two different tasks, IE and OD. Experimental results show that IA2U combined with an in-air model can achieve superior performance in underwater image enhancement and object detection tasks. The code will be made publicly available.

翻译：在水下环境中，悬浮颗粒浓度与浊度的变化会导致图像严重退化，为图像增强(IE)与目标检测(OD)任务带来重大挑战。当前，空中图像增强与检测方法已取得显著进展，但受限于水下环境的复杂性与多变性，这些方法在实际应用中面临诸多局限。相较于从零构建水下模型，微调空中模型不仅可降低高昂开销，还能获得更丰富的参考工作。为解决上述问题，我们设计了一种融合多先验的迁移插件IA2U，用于将空中模型适配至水下应用场景。IA2U通过整合三类水下先验实现高效部署：表征图像退化程度（如色彩与可见度）的水质类型先验、聚焦细节与纹理差异的退化先验，以及综合考虑拍摄时环境条件与目标物体特征的样本先验。该插件采用类Transformer结构，将先验信息作为查询条件，结合联合任务损失函数，实现任务级水下图像特征的分层增强，从而兼顾图像增强与目标检测这两类差异化任务的需求。实验表明，IA2U与空中模型结合后，在水下图像增强与目标检测任务中均能展现出卓越性能。相关代码将开源发布。