Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement

• URL: http://arxiv.org/abs/2102.00670v1
• Date: Mon, 1 Feb 2021 07:13:39 GMT
• Title: Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement
• Authors: Zhenqi Fu, Xueyang Fu, Yue Huang, and Xinghao Ding
• Abstract summary: We propose a rank learning guided no-reference quality assessment method for underwater image enhancement (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. We conduct extensive experiments on both synthetic and real-world datasets.
• Score: 42.03072878219206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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