Tian Fei, Xue Jiuming, Shi Zhedong, Luo Hongling, Cai Wanyuan, Tao Wei
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Shanghai Institute of Satellite Engineering, Shanghai, 200240, China.
Sci Rep. 2025 Jan 31;15(1):3922. doi: 10.1038/s41598-025-86529-3.
Underwater imaging is significant but the images are always subject to degradation, which varies in different underwater environments. Factors such as light scattering, absorption, and environmental noise can affect the quality of underwater images, leading to issues such as color shift, low contrast, and low definition. Polarization information benefits image recovery, and existing learning-based polarimetric imaging methods ignore multiple water types (viewed as domains) and domain generalization. In this paper, we collect, to the best of our knowledge, the richest polarization color image dataset with different water types and present a specially designed neural network UPD-Net firstly employing the domain-adversarial learning strategy to recover the degraded color images. The designed water-type classifier and domain-adversarial learning strategy enable the multi-encoder to output domain-independent features, the decoder outputs clear images consistent with the ground truths with the help of the discriminator and generative-adversarial learning strategy, and there is another decoder responsible for outputting DoLP image. Comparison experiments demonstrate that our method is state-of-the-art in terms of visual effect and value metrics and that it has a strong recovery ability in the source and unseen domains, including in water with high turbidity. The proposed approach has significant potential for underwater imaging and recognition applications in varied underwater environments.
水下成像意义重大，但图像总是容易退化，且在不同水下环境中退化情况各异。光散射、吸收以及环境噪声等因素会影响水下图像质量，导致诸如色彩偏移、对比度低和清晰度低等问题。偏振信息有助于图像恢复，而现有的基于学习的偏振成像方法忽略了多种水类型（视为域）和域泛化。在本文中，据我们所知，我们收集了包含不同水类型的最丰富的偏振彩色图像数据集，并首先提出了一种专门设计的神经网络UPD-Net，该网络采用域对抗学习策略来恢复退化的彩色图像。所设计的水类型分类器和域对抗学习策略使多编码器能够输出与域无关的特征，解码器借助判别器和生成对抗学习策略输出与真实情况一致的清晰图像，并且还有另一个解码器负责输出偏振度（DoLP）图像。对比实验表明，我们的方法在视觉效果和数值指标方面处于领先水平，并且在源域和未见域中具有很强的恢复能力，包括在高浊度水中。所提出的方法在各种水下环境中的水下成像和识别应用方面具有巨大潜力。
