An underwater image enhancement strategy based on pyramid attention mechanism

Jianxin Feng; Yajun Han

doi:10.1117/12.2689089

31 July 2023 An underwater image enhancement strategy based on pyramid attention mechanism

Jianxin Feng, Yajun Han

Proceedings Volume 12747, Third International Conference on Optics and Image Processing (ICOIP 2023); 127471H (2023) https://doi.org/10.1117/12.2689089
Event: Third International Conference on Optics and Image Processing (ICOIP 2023), 2023, Hangzhou, China

Abstract

Due to the low contrast and color distortion of underwater images caused by the absorption and scattering of light by water, this paper proposes an underwater image enhancement strategy based on the attention mechanism of pyramids. Based on FUnIE-GAN, the feature extraction module uses MobileNet to replace the VGG16 model in the original U-Net structure, which reduces the number of network model parameters and accelerates the inference speed of the network model. Furthermore, the pyramid attention module is introduced into the generative network. The multi-scale pyramid feature and attention mechanism collection is beneficial to enhance the network feature extraction ability and improve the model performance. Experiments are carried out on EUVP dataset. The results show that the underwater images enhanced by our method are better in terms of subjective and objective aspects and have improved sharpness, color correction and contrast. The average values of peak signal-to-noise ratio and structural similarity were 21.398 and 0.742, respectively that is better than the other comparison methods.

Citation Download Citation

Jianxin Feng and Yajun Han "An underwater image enhancement strategy based on pyramid attention mechanism", Proc. SPIE 12747, Third International Conference on Optics and Image Processing (ICOIP 2023), 127471H (31 July 2023); https://doi.org/10.1117/12.2689089

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KEYWORDS

Image enhancement

Feature extraction

Image processing

Image quality

Gallium nitride

Data modeling

Education and training

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