Optimizing underwater image enhancement: integrating semi-supervised learning and multi-scale aggregated attention

Abstract

Underwater image enhancement is critical for advancing marine science and underwater engineering. Traditional methods often struggle with color distortion, low contrast, and blurred details due to the challenging underwater environment. Addressing these issues, we introduce a semi-supervised underwater image enhancement framework, Semi-UIE, which leverages unlabeled data alongside limited labeled data to significantly enhance generalization capabilities. This framework integrates a novel aggregated attention within a UNet architecture, utilizing multi-scale convolutional kernels for efficient feature aggregation. This approach not only improves the sharpness and authenticity of underwater visuals but also ensures substantial computational efficiency. Importantly, Semi-UIE excels in capturing both macro- and micro-level details, effectively addressing common issues of over-correction and detail loss. Our experimental results demonstrate a marked improvement in performance on several public datasets, including UIEBD and EUVP, with notable enhancements in image quality metrics compared to existing methods. The robustness of our model across diverse underwater environments is confirmed by its superior performance on unlabeled datasets. Our code and pre-trained models are available at https://github.com/Sunhan-Ash/Semi-UIE.