Abstract
The challenges in recovering underwater images are the presence of diverse degradation factors and the lack of ground truth images. Although synthetic underwater image pairs can be used to overcome the problem of inadequately observing data, it may result in over-fitting and enhancement degradation. This paper proposes a model-based deep learning method for restoring clean images under various underwater scenarios, which exhibits good interpretability and generalization ability. More specifically, we build up a multi-variable convolutional neural network model to estimate the clean image, background light and transmission map, respectively. An efficient loss function is also designed to closely integrate the variables based on the underwater image model. The meta-learning strategy is used to obtain a pre-trained model on the synthetic underwater dataset, which contains different types of degradation to cover the various underwater environments. The pre-trained model is then fine-tuned on real underwater datasets to obtain a reliable underwater image enhancement model, called MetaUE. Numerical experiments demonstrate that the pre-trained model has good generalization ability, allowing it to remove the color degradation for various underwater attenuation images such as blue, green and yellow, etc. The fine-tuning makes the model able to adapt to different underwater datasets, the enhancement results of which outperform the state-of-the-art underwater image restoration methods. All our codes and data are available at \url{this https URL}.
Abstract (translated)
恢复水下图像面临的挑战包括存在多种退化因素和缺乏准确的照片。虽然合成的水下图像对解决数据观察不足的问题有所帮助,但可能会导致过度拟合和增强退化。本文提出了基于模型的深度学习方法,用于在不同水下场景下恢复清洁的图像,表现出良好的解释性和泛化能力。具体来说,我们构建了一个多变量卷积神经网络模型,用于估计清洁图像、背景光和传输地图。高效的损失函数也被设计为紧密集成基于水下图像模型的变量。使用元学习策略,我们获取了在合成水下数据集上的预训练模型,该数据集包含不同类型的退化,以涵盖各种水下环境。预训练模型随后在真实水下数据集上进行微调,以获得一种可靠的水下图像增强模型,称为MetaUE。数值实验表明,预训练模型具有良好的泛化能力,使其能够去除各种水下衰减图像(如蓝色、绿色和黄色)的颜色退化。微调使模型能够适应不同的水下数据集,增强结果超过了最先进的水下图像恢复方法。我们的代码和数据都在 url{this https URL} 上可用。
URL
https://arxiv.org/abs/2303.06543
