Feature attention network (FA-Net): a deep-learning based approach for underwater single image enhancement

Muhammad Hamza; Ammar Hawbani; Sami Ul Rehman; Xingfu Wang; Liang Zhao

doi:10.1117/12.2644516

12 October 2022 Feature attention network (FA-Net): a deep-learning based approach for underwater single image enhancement

Muhammad Hamza, Ammar Hawbani, Sami Ul Rehman, Xingfu Wang, Liang Zhao

Author Affiliations +

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123421L (2022) https://doi.org/10.1117/12.2644516
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Underwater image processing and analysis have been a hotspot of study in recent years, as more emphasis has been focused to underwater monitoring and usage of marine resources. Compared with the open environment, underwater image encountered with more complicated conditions such as light abortion, scattering, turbulence, nonuniform illumination and color diffusion. Although considerable advances and enhancement techniques achieved in resolving these issues, they treat low-frequency information equally across the entire channel, which results in limiting the network's representativeness. We propose a deep learning and feature-attention-based end-to-end network (FA-Net) to solve this problem. In particular, we propose a Residual Feature Attention Block (RFAB), containing the channel attention, pixel attention, and residual learning mechanism with long and short skip connections. RFAB allows the network to focus on learning high-frequency information while skipping low-frequency information on multi-hop connections. The channel and pixel attention mechanism considers each channel's different features and the uneven distribution of haze over different pixels in the image. The experimental results shows that the FA-Net propose by us provides higher accuracy, quantitatively and qualitatively and superiority to previous state-of-the-art methods.

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Muhammad Hamza, Ammar Hawbani, Sami Ul Rehman, Xingfu Wang, and Liang Zhao "Feature attention network (FA-Net): a deep-learning based approach for underwater single image enhancement", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123421L (12 October 2022); https://doi.org/10.1117/12.2644516

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KEYWORDS

Image enhancement

Air contamination

RGB color model

Image analysis

Radiofrequency ablation

Signal attenuation

Image fusion

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