  To improve the quality of underwater images, various kinds of underwater
image enhancement (UIE) operators have been proposed during the past few years.
However, the lack of effective objective evaluation methods limits the further
development of UIE techniques. In this paper, we propose a novel rank learning
guided no-reference quality assessment method for UIE. Our approach, termed
Twice Mixing, is motivated by the observation that a mid-quality image can be
generated by mixing a high-quality image with its low-quality version. Typical
mixup algorithms linearly interpolate a given pair of input data. However, the
human visual system is non-uniformity and non-linear in processing images.
Therefore, instead of directly training a deep neural network based on the
mixed images and their absolute scores calculated by linear combinations, we
propose to train a Siamese Network to learn their quality rankings. Twice
Mixing is trained based on an elaborately formulated self-supervision
mechanism. Specifically, before each iteration, we randomly generate two mixing
ratios which will be employed for both generating virtual images and guiding
the network training. In the test phase, a single branch of the network is
extracted to predict the quality rankings of different UIE outputs. We conduct
extensive experiments on both synthetic and real-world datasets. Experimental
results demonstrate that our approach outperforms the previous methods
significantly.
