  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.
