Underwater Image Enhancement via Learning Water Type Desensitized Representations

For underwater applications, the effects of light absorption and scattering result in image degradation. Moreover, the complex and changeable imaging environment makes it difficult to provide a universal enhancement solution to cope with the diversity of water types. In this letter, we present a novel underwater image enhancement (UIE) framework termed SCNet to address the above issues. SCNet is based on normalization schemes across both spatial and channel dimensions with the key idea of learning water type desensitized features. Considering the diversity of degradation is mainly rooted in the strong correlation among pixels, we apply whitening to de-correlates activations across spatial dimensions for each instance in a mini-batch. We also eliminate channel-wise correlation by standardizing and re-injecting the first two moments of the activations across channels. The normalization schemes of spatial and channel dimensions are performed at each scale of the U-Net to obtain multi-scale representations. With such latent encodings, the decoder can easily reconstruct the clean signal, and unaffected by the distortion types caused by the water. Experimental results on two real-world UIE datasets show that the proposed approach can successfully enhance images with diverse water types, and achieves competitive performance in visual quality improvement.

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

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.