  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
https://github.com/Huang-ShiRui/Semi-UIR.
