Dual-attention global domain adaptation for mariculture image enhancement

Abstract

Mariculture image enhancement aims to recover degraded images and meet the requirements of various digital aquaculture systems. However, the existing underwater image enhancement (UIE) cannot suit diverse marine scenarios and leads to sub-optimal results for the real mariculture images. To solve the aforementioned issues, a novel dual-attention global domain-adaptive mariculture image enhancement network (DAMIE) is proposed to improve the quality of degraded images. Specifically, the proposed method consists of two core parts: (1) an innovative depth transfer dual-attention module to aggregate multiple features and bridge the difference between domains; (2) a modified encoder–decoder enhancement network with a global feature vector to reconstruct clean mariculture images. Meanwhile, a semi-supervised adaptive training scheme is utilized to improve the model generalization in different mariculture domains. Extensive experiments demonstrate that the proposed DAMIE can achieve a good performance in terms of quantitative and qualitative metrics. In addition, an ablation study is conducted to analyse the contribution of the key components in the proposed model.