Twice Mixing: A rank learning based quality assessment approach for underwater image enhancement

Abstract

Objectively and accurately evaluating underwater images generated by different enhancement algorithms is an essential issue, which however is still largely under-explored. In this paper, we present a novel rank learning guided no-reference quality assessment method to evaluate different underwater image enhancement (UIE) algorithms. It is also the first work that utilizes deep learning approaches to address this problem. Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image and its low-quality version. 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 utilized 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. Additionally, to train our network, we construct a new dataset that contains over 2200 raw underwater images and their high/low-quality versions. Twice Mixing is evaluated on both synthetic and real-world datasets. Experimental results show that the proposed approach outperforms the previous methods significantly.