IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater

• URL: http://arxiv.org/abs/2312.06955v2
• Date: Fri, 19 Jan 2024 01:47:22 GMT
• Title: IA2U: A Transfer Plugin with Multi-Prior for In-Air Model to Underwater
• Authors: Jingchun Zhou and Qilin Gai and Kin-man Lam and Xianping Fu
• Abstract summary: In underwater environments, variations in suspended particle concentration and turbidity cause severe image degradation. We develop a transfer plugin with multiple priors for converting in-air models to underwater applications, named IA2U. We show that IA2U combined with an in-air model can achieve superior performance in underwater image enhancement and object detection tasks.
• Score: 18.491734287988304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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