Despite the remarkable achievement of recent underwater image restoration
techniques, the lack of labeled data has become a major hurdle for further
progress. In this work, we propose a mean-teacher based Semi-supervised
Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled
data into network training. However, the naive mean-teacher method suffers from
two main problems: (1) The consistency loss used in training might become
ineffective when the teacher's prediction is wrong. (2) Using L1 distance may
cause the network to overfit wrong labels, resulting in confirmation bias. To
address the above problems, we first introduce a reliable bank to store the
"best-ever" outputs as pseudo ground truth. To assess the quality of outputs,
we conduct an empirical analysis based on the monotonicity property to select
the most trustworthy NR-IQA method. Besides, in view of the confirmation bias
problem, we incorporate contrastive regularization to prevent the overfitting
on wrong labels. Experimental results on both full-reference and non-reference
underwater benchmarks demonstrate that our algorithm has obvious improvement
over SOTA methods quantitatively and qualitatively. Code has been released at
this https URL

本研究提出了一种基于均值教师的半监督水下图像恢复框架，利用可靠性指标来选择最值得信任的负责伪标记的模型，并引入对比度正则化来防止对错误标签过拟合。实验结果表明，我们的算法在完全参考和非参考水下基准测试中，从定量和定性上都比 SOTA 方法有明显的改进。